The current paper presents many aspects of a complete research, with the following steps in the development of applications:

1) Object detection by common methods (YOLO, ROBOFLOW) and with images of real objects in data-sets;

2) Object detection by improved methods (A + CNN) for better confidence of results;

3) Object detection with image data-sets from CAD models;

4) Advanced labeling for additional information;

5) Special CAD application for computer vision.

Each previous step was developed to improve the overall detection efficiency in various aspects in object detection of complex assemblies and orientation of objects in 2D and 3D space.

The global efficiency of the object recognition techniques depends on:

1) Neural Network technique implemented: ANN, CNN, or other technique with additional methods to increase efficiency;

2) The number of the reference objects in the database (Ex: pretrained COCO 2017 dataset has 330k images);

3) The images size for each reference (32 × 32, 200 × 200, 320 × 320, 640 × 640, 768 × 768, 1024 × 1024, 1280 × 1280, 1536 × 1536);

4) Database format (TF, ONNX, etc.);

5) Number of classes implemented in database;

6) Software and version used to test (CNET, Local Python, etc.);

7) Additional libraries used to test (additional libraries to manage structures, to process images, etc.);

8) CPU, GPU and RAM capabilities;

9) RUN mode, latency;

10) Number of Epochs (Ex: 300);

11) Original image size and object scale;

12) Scene type: image or video;

13) Inference performances;

14) Fine tune: Detection, Segmentation, Classification, Pose or OBB;

15) Number of detections in one frame;

16) Image quality and augmentation processing: colors, illumination, contrast, HDR, adaptive contrast, black-hole-areas, color channels and swap;

17) Partial scanning of areas can improve the quality of object detection;

18) Labeling definitions and information stored for additional data;

19) Complexity of labeling implemented system: amount of information, structured data, etc.;

20) Percentages for Training Set, Validation Set and Test Sets;

21) Proper selection of references for training, validation or test groups: manually, random or specific techniques for selection;

22) Additional references in set based on images processing: rotate, skew, etc.;

23) Additional references in custom set based on augmented images processing;

24) Additional images based on 3D object CAD design.

A particular aspect of object detection in medical applications is the detection of small objects. This issue is a computer vision problem because we aim to accurately identify objects that are small in a video feed or images, even if each object is small relative to the photo size.

Detecting small objects is hard because detection models form specific features by aggregating pixels in convolutional layers. The most successful object detection techniques recommend higher-resolution images, even with a size of larger than 1280px. The management of such a dataset became difficult using custom computers.

Every new object detection technique comes with additional parameters that need to be set for better global or local performance. A major problem in computer image processing is the multiple data required to compute in cloud. This multiple datum depends on the areas to be observed and several parameters for image quality and object detection performance.

Some detection techniques are used instead of running on the whole scene, to split it into smaller parts (slices), run the model on each one, and then stitch the results together. This method even took much more time, the model gives better detection performances. Such a technique is SAHI (Slicing-Aided Hyper-Inference).

There are few references for surgical tools in common database sets; most of them are particular reference objects. For comparison reasons, the object detection model was also tested using YOLO11 and the ROBOFLOW online platform. Convolutional Neural Networks (CNNs) were initially developed as standardized structures—fully connected stacked convolutional layers [14] .

Various AI techniques were developed to achieve models with higher performance in object detection. From object detection technique in a single pass for speed (YOLO = You Only Look Once) with various sub-methods for improvements in recognition based on augmentation, scaling, the fusion of features between layers, the technique for optimization of memory used or semantic seg-mentation capabilities was only a few improvements during years in developed on YOLO methods [15] .

In contrast, two-stage or multi-stage methods, such as Region-based Convolutional Neural Networks (R-CNNs), first generate region proposals and only then perform classification. These methods offer high precision but are computationally expensive in resources and time for study. Convolutional Neural Networks techniques that use studies on regions came to helping in decomposition and to improve overall detection problem performances, but also to split the global problem into a few other sub-problems easier to manage [16] .

Improved methods were also developed over the years to increase the efficiency of object detection studies, like feature extraction, methods for transfer learning, or import and export of neural network models between various platforms [17] .

Various on-line applications were also developed for researchers to study the object detection techniques, for labeling images, and to test their models.

The advantages of using common detection applications and models are the possibility of developing new models in the future with more connected users, more references saved in a dataset and using a specific standard.

An example with object detection test using YOLO pretrained dataset is presented in Figure 2 , which shows that common applications and datasets have a limited number of classes and references for a specific object. Only one type of class object can be recognized using pre-trained datasets.

The use of common applications can also limit us through certain constraints in developing our research methods, limitations in the way of presenting and providing information regarding the objects subject to detection, limitations in imposing our objective functions, limitations in using a specific number of hidden layers, as well as limitations in their connectivity.

Reusing a pre-trained network is not the best method for our intention to detect specific objects. The first reason is the limited number of references and label information in datasets for our objects [18] [19] .

Figure 3 shows a partially custom dataset that uses 300 images were initially split into 3 classes: Fenestrated forceps, Needles driver, and Scissors.

Using custom dataset and YOLO11 training was obtained the results from Figure 4 . For this image, object detection has no results using pretrained YOLO11, Google NET or ROBOFLOW applications.

It was also a big challenge to thoroughly verify the performance of our object detection model. Even established applications can have data processing errors for custom models. There should be checked bounding boxes, selection scaling, anchors, labeling, etc., for almost every AI training. Object detection training performances using custom model obtained with ROBOFLOW are shown in Figure 5 .

During testing was observed also the possibilities to detect more than one object. This problem is due to the initial image set defined with labeling areas with too obvious similarities between object classes. A such example is shown in Figure 6 .

For problems of similarities between various areas of various objects, the initial set was redefined with 5 classes like in Figure 7 :

1) Fenestrated forceps;

2) Needles driver;

3) Scissors;

4) Surgery stick;

5) Gears.

Performances in object detection was also extracted from custom training using YOLO11. Figure 8 shows some specific parameters from training of the custom AI model with 5 classes. A special representation of YOLO procedure is the Auto-test prediction for used custom dataset ( Figure 8(c) ).

Even though the dataset does not have too many images, and therefore the division between test, training, and validation images leads to even fewer images, it is noticeable that for some objects, the auto-test prediction is quite perfect (90%). The rest of the information of the training needs to be analyzed to add more images to the AI model and for future improvements.

The paper doesn’t focus on a benchmark between YOLO and ROBOFLOW. We want to have a comparison only at confidence results obtained with ROBOFLOW-web-app and YOLO-local-app. Any other type of parameter for comparisons between YOLO and ROBOFLOW was not our concern.

Limitations in common object detections observed during testing led to develop a method to improve the efficiency of the object recognition techniques.

One of the major problems observed in object detection results was the low performance in case of difficult capture conditions related to object illuminations and contrast.

For this reason, during this research an improved CNN technique developed was to implement preprocessing methods that use adaptive contrast and adaptive illumination for input images to improve segmentation [20] - [23] . In the next sentences for this method, we refer to A + CNN (Adaptive plus CNN).

Figure 9 shows object detection using A + CNN based on custom CAD data-set with 5 classes. Contrast and illumination of initial image was improved before detection.

A + CNN technique can be applied also inside layers if layers use selective areas for object detection because final results in adaptive contrast and illuminations depend on the areas selected for processing. These results will be explained in future papers.

Because the best explanation is given by images, in Figure 10 , a few examples are presented in using CNN with adaptive illumination and adaptive contrast in technique for colorization of noir images.

One of the most common object orientation detection techniques is based on OBB-Detection, an oriented object detection library, which is based on the MM-detection project [24] . The Oriented Bounding Boxes (OBBs) object detection is used to detect objects oriented at various angles. The output is a set of rotated bounding boxes that exactly enclose the objects in the image, along with class labels and information data (angle, confidence, “size” of object, contour area, etc.).

Why is object orientation important for us? Because the main goal of this research is to detect specific instruments in the surgical scenes, their position, and the direction of action of these instruments. Figure 11 shows two types of orientation labelling info that can be used during detection: bounding box and local coordinate system.

Object orientation detection can be evaluated based on the matrix transformation between the input and the reference. Object orientation detection focuses on identifying key features, such as edges, corners, or specific shapes, to under-stand the orientation of the object and to highlight the most important such features. Verifying the alignment of an object involves ensuring that it is exactly where it is supposed to be or should be, with the correct position and direction, including with all necessary corrective measures that depend on the viewing angle of the focused object.

The position and alignment of an object can require varying different values of precision depending on the field of activity. For example, in production, even a small misalignment can cause product defects. Similarly, in robotics, precise positioning is required for tasks such as accurately reaching a specific target location. In the medical field, precise localization and orientation are understood to be even more important.

The object orientation detection method even allows a selective assessment of particular objects with specific information (see Figure 12 ) based on specific requirements: confidence, angle, size, object color, etc. [25] - [31] . An example of using AI object detection in the evaluation of distances and object sizes is presented in the paper [32] .

The object orientation can even be represented selectively when we focus only on specific classes. An example is shown in Figure 12 .

At the end of any study, there is always a need to obtain the maximum amount of information with as little technical documentation as possible. One of such technique is an advanced labeling method for object detection.

Advanced labeling is a combination of information about classification, confidence, localization, orientation, spatial orientation, tracking, etc. that can be done with a custom application for object detection. Figure 13 shows a such example where some results with confidence depend on classes. The global confidence is between 61% for gear, 83% for stick, and 78% for forceps.

One of the main issues in object detection is the limited number of references in dataset. Preparing the images and labeling also takes time for pre-processing.

This sub-chapter shows an innovative method of using CAD models to obtain images for object detection techniques.

The reference images for AI training are obtained from CAD models of focused objects for detection. Figure 14 shows a comprehensive list of 60 images from a dataset for AI training with only two classes. Using images obtained from a CAD model of focused objects for a custom dataset is a good method when there are not enough images of the real object. Results from object detection using ROBOFLOW can be viewed in Figure 15 .

Using only CAD-set images was successful even for some difficult images for testing in object detection, like the image from Figure 16 .

The CAD-set can be combined with the real images dataset to complement each other.

In all situations, it appears a necessity to have also a different view from the scene, a different view angle, or a different view direction, because of insufficient data about the object from the scene when using limited number of object views.

This issue can be improved by using stereoscopic cameras. In Figure 17 , two types of stereo-devices can be viewed used to capture stereoscopic images.

Figure 18 shows a CAD design of a surgical robot that use four video cameras (quad-scope view). This concept has special methods implemented for spatial evaluation of the scene.

After obtaining real success with CAD-dataset in object detection, we propose in this sub-chapter also a CAD application add-on special designed for computer vision technique used in object detection. By controlling the CAD models and using special applications for object detection, we actually obtain software for CAD detection. This method is also an innovative method for object detection.

Disadvantages of CAD-dataset:

1) Is needed to define in CAD model of every object for detection;

2) Modeling in CAD is taking more time than a single picture;

3) Real images offer more realistic view of the object;

4) Hard to code for almost engineers.

Advantages of CAD application in object detection:

1) Is not necessary to store images of the objects. In this case is stored the CAD models of the objects;

2) No limitations in number of pose of objects. Any poses of objects can be obtained using CAD application;

3) CAD models take up a small portion of space on your HDD, much smaller than the size of all the images in the dataset;

4) CAD object offer much more detailed information about object (pose position, orientation, size, zooming, etc.);

5) CAD applications ( Figure 19 ) in object detection also help us benefit from all the capabilities of a genuine CAD system: viewing objects in any direction, augmented visualization, improved color, adequate lighting, object sectioning, zooming at any scale, exploding of assemblies, transparency of specific areas, etc.;

6) CAD models can be created now very quick using ultrasonic scanning methods.

Figure 19 shows a special application developed also for computer vision. Managing the local coordinate systems orientation, we obtain the necessary similarity between a real object and its CAD twin.

Reconstruction in 3D of scenes and the development from now of digital twins will lead to increased interest in CAD models for every object around us. 3D re-construction is applied very often in medical domain today [29] .

Results for few object detection cases are presented in Figure 20 and Figure 21 . The images were collected from various sources on the www, processed and labeled for this research. Confidence varies from 6% in Figure 20(c) to 71% in Figure 20(d) . AI test was made with ROBOFLOW for (a, c, and d) and with YOLO11 in image (b).

Object detection results from Figure 21 are made with A + CNN for (a), and ROBOFLOW for (b, c, and d).

As we can observed during this paper, a ROBOFLOW was preferred. Even is an online web application ROBOFLOW has capability to create a quick AI training for custom data-set because the solving is made on multiple servers with multiple processors. In comparison, a local AI training with YOLO takes 10 hours for all images custom set, and with ROBOFLOW was 10 times much faster.

Based on CAD application to create CAD objects of similar real object, was developed also a new innovative method for using directly CAD in object detection. Application presented in Figure 1 to control CAD assembly orientation was extended with functions to compare virtual CAD models with real images of focused object. The application was developed under SOLIDWORKS-addon and with connectivity capabilities to run other external codes (MATLAB or PYTHON).

Connectivity between MATLAB, PYTHON, EXE, and SW add-ons is achieved directly through DDE (Dynamic Data Exchange) capabilities, STDIN/STDOUT capabilities, or any other user-defined data transfer type.

Figure 22 shows object detection results using directly CAD application. We can observe in Figure 22(b) the errors in 2D angle orientation in comparison with Figure 13 , and also advanced labelling for 3D spatial orientation (Euler Angles).

The CAD application for object detection has two types of assessments:

A) Search assembly CAD orientation and compare the camera image with focused object for detection;

B) Use AI in image comparison between target image and an internal data-set of CAD assembly internally and locally generated.

For the method from point A), was coded Multi-scale Structural Similarity Index Measure (MS-SSIM method) implemented in Python as an external application for the main CAD-addon. The MS-SSIM method is very accurate and sensitive in image processing [33] . Because the methodology is under-development, as acceptance criterion was used SSIM score higher than 50%. For example, between images from Figure 22 , 66% SSIM score was obtained. There are more methods to use for improvements in images comparisons, which will be evaluated into the future research [34] .

Validation of the CAD-dataset can be done also using real images dataset. By reverse engineering from real images to CAD-set or CAD-addon can improve research evaluation of object detection, classification, segmentation or orientation.

The combination between the real images set and the SOLIDWORKS application (virtual model) is a real, fully functional, Digital-Twins.

Figure 23 shows object detection results when was used two images in the same detection session. These two images are obtained from a stereoscopic device from Figure 17 . But this technique can be applied also using multiple cameras, with more success for detection and improvements in global efficiency.

It can be observed that there are some differences between the left and right images if we are using A + CNN object detection technique.