YOLOv5s_2E: Improved YOLOv5s for Aerial Small Target Detection

• Published in: IEEE access 2023, Vol.11, p.80479-80490
• Main Authors: Shi, Tao, Ding, Yao, Zhu, Wenxu
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Clustering; Data augmentation; Datasets; DyHead; Object recognition; small object detection; soft_NMS; Target detection; YOLOv5s
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3300372

To address the issues of low accuracy in existing small object detection algorithms, an improved network model algorithm called YOLOv5s_2E is proposed. This method first uses the k-means++ clustering algorithm to calculate the prior boxes of the Visdrone dataset. Then, it introduces Soft_NMS and combines it with EIoU to propose the EIoU_Soft_NMS algorithm to replace the non-maximum suppression (NMS) of the original network, improving the detection of objects that are occluded. The bounding box regression loss function uses Focal-EIoU, which speeds up model convergence and reduces loss. Additionally, a detection layer is added to the original detection head to unify the channel numbers, and with the dynamic head framework DyHead, the attention mechanism is integrated with the detector’s head to further improve small object detection accuracy. Finally, the system robustness is improved by adjusting the ratio of data augmentation methods Mixup and Mosaic.Experimental results show that the proposed algorithm improves the mAP@0.5, mAP@0.5:0.95 and detection accuracy by 12.6%, 12.2%, and 20.5%, respectively, compared to the previous method on the VisDrone dataset. The parameter size only increases by 4%, and the weight file size increases by only 0.57MB, meeting the accuracy requirements for small object detection.