Efficient small target detection system with composite multi-scale detection head

• Published in: Journal of electronic imaging Vol. 34; no. 3; p. 033036
• Main Authors: Ma, Yue, Yang, Ye, Zhu, Yongchang, Yang, Sen, Wang, Zenghui, Tong, Jigang
• Format: Journal Article
• Language: English
• Published: Society of Photo-Optical Instrumentation Engineers 01.05.2025; SPIE
• Subjects: Algorithms; Detectors; China; spatial pyramid pooling fast; object detection; multi-scale detectors; decoupled detectors; small target detection; YOLOv7
• ISSN: 1017-9909; 1560-229X
• DOI: 10.1117/1.JEI.34.3.033036

In real-world complex scenarios, challenges are faced by object detection due to factors such as significant variations in target scale, similarity with the background, dense and overlapping instances, and small-sized targets. To address these challenges, we optimized the detector head part through the employment of decoupled detector head and auxiliary detector head algorithms. The classification and localization tasks are partitioned by these two algorithms, and the sample quality is enhanced by the auxiliary detector head to improve accuracy. To solve the information loss of small targets in the model downsampling, the residual structure and loss function are optimized so that the feature information of small targets is retained. Moreover, a multi-scale path aggregation network and a multi-scale detection head are adopted to retain local feature information while focusing on global features. Furthermore, dense scenes and small targets are adapted to by the integration of the anchor-based algorithm and the introduction of a loss algorithm, and more accurate localization is facilitated. Experimental results show a 1.7% increase in mAP on the MS COCO dataset, indicating improved performance in complex real-life scenes. A 7.2% enhancement on the TCOD dataset is shown, indicating better performance in densely clustered or overlapping scenes and small target detection. Challenges such as variations in target size, background similarity, dense and overlapping instances, and small-sized targets are tackled, and the accuracy and robustness of object detection are improved. The practical applications of the proposed approach are expanded.