TITLE:
CASA-YOLO: A Unified Framework for Small and Camouflaged Object Detection in Agricultural Pest Imagery
AUTHORS:
Koffi Bernadin-Pacome Sayni, Apo Chimène Monsan, Mamadou Diarra, Beman Hamidja Kamagaté, Souleymane Oumtanaga
KEYWORDS:
Object Detection, Small Object Detection, Camouflaged Object Detection, Attention Mechanism, Agricultural Computer Vision, Deep Learning, Pest Detection, Cashew Tree
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.6,
June
17,
2026
ABSTRACT: Small object detection (SOD) and camouflaged object detection (COD) are critical challenges in agricultural computer vision, where pests exhibit both spatial compactness and visual similarity to their surroundings. Existing approaches address these problems in isolation, failing to exploit their shared characteristic: extracting weak visual signals from low signal-to-noise environments. This paper introduces CASA-YOLO (Context-Aware Sparse Attention YOLO), a unified framework addressing SOD and COD through three innovations: 1) Dual-Axis Sparse Attention (DASA), which decomposes global attention into axis-wise operations with adaptive sparse sampling, reducing complexity from O(
N
2
) to O(
N
N
/s
); 2) Adaptive Context Gating (ACG), a three-pathway module dynamically balancing local texture, global semantics, and boundary cues; and 3) HFPN-Nano, a hierarchical feature pyramid enabling stride-4 detection of objects as small as 8 × 8 pixels. On the AgroPest-12 benchmark, CASA-YOLO achieves 89.6% mAP@50 and 58.3% mAP@50 - 95, surpassing YOLOv11s (+5.9% mAP@50) and RT-DETR-R18 (+3.3%) at FP32 precision, while maintaining real-time inference (118 FPS with TensorRT INT8 quantization). Field validation on cashew plantations across three regions in C?te d’Ivoire (895 images, 8 sites) confirms practical applicability. Camouflage-stratified analysis further shows that ACG provides significant gains on high-camouflage instances, validating the unified SOD-COD design philosophy for agricultural pest detection.