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DDH-YOLOv5: improved YOLOv5 based on Double IoU-aware Decoupled Head for object detection

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Abstract

YOLOv5 is a high-performance real-time object detector that plays an important role in one-stage detectors. However, there are two problems with the design of the YOLOv5 head. The common branch of classification task and regression task of the YOLOv5 head will hurt the training process, and the correlation between classification score and localization accuracy is low. We propose a Double IoU-aware Decoupled Head (DDH) and apply it to YOLOv5. The improved model is named DDH-YOLOv5, which substantially improves the localization accuracy of the model without significantly increasing FLOPS and parameters. Extensive experiments on dataset PASCAL VOC2007 show that DDH-YOLOv5 has good performance. Compared with YOLOv5, DDH-YOLOv5m and DDH-YOLOv5l proposed in this paper achieve 2.4\(\%\) and 1.3\(\%\) improvement in Average Precision (AP), respectively. Compared with Deformable DETR, which is known for its fast-converging, DDH-YOLOv5 completely outperforms Deformable DETR on COCO2017 Val with half of FLOPS and only a quarter of epochs.

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Acknowledgements

This work was supported by the Jilin Province Science and Technology Department Science and Technology Development Planning Project of China (YDZJ202201ZYTS556), and the Jilin Province Education Department Scientific Research Planning Project of China (JJKH20210753KJ).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Changchun University of Technology, Changchun, 130051, Jilin, China

    Hui Wang, Yang Jin & Xinping Zhang

  2. School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, 130012, Jilin, China

    Hongchang Ke

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Correspondence to Hongchang Ke.

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Wang, H., Jin, Y., Ke, H. et al. DDH-YOLOv5: improved YOLOv5 based on Double IoU-aware Decoupled Head for object detection. J Real-Time Image Proc 19, 1023–1033 (2022). https://doi.org/10.1007/s11554-022-01241-z

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