DI-YOLOv5: An Improved Dual-Wavelet-Based YOLOv5 for Dense Small Object Detection

Abstract

This letter focuses on the fact that small objects with few pixels disappear in feature maps with large receptive fields, as the network deepens, in object detection tasks. Therefore, the detection of dense small objects is challenging. A DI-YOLOv5 object detection algorithm is proposed. Specifically, a dual-wavelet convolution module (DWCM), which contains DWT_Conv and IWT_Conv, is proposed to reduce the loss of feature map information while obtaining feature maps with a large receptive field. The DWT _ Conv and IWT _ Conv can be used as replacements for downsampling and upsampling operations. Moreover, in the process of information transmission to the deep layer, a CSPCoA module is proposed to further capture the location information and information dependencies in different spatial directions. DWCM and CSPCoA are single, generic, plug-and-play units. We propose DI-YOLOv5 with YOLOv5 [1] as the baseline, and extensively evaluate the performance of these two modules on small object detection. Experiments demonstrate that DI-YOLOv5 can effectively improve the accuracy of object detection.