Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution

Abstract

The rapid advancement of deep learning has significantly accelerated progress in target detection. However, the detection of small targets remains challenging due to their susceptibility to size variations. In this paper, we address these challenges by leveraging the latest version of the You Only Look Once (YOLOv7) model. Our approach enhances the YOLOv7 model to improve feature preservation and minimize feature loss during network processing. We introduced the Spatial Pyramid Pooling and Cross-Stage Partial Channel (SPPCSPC) module, which combines the feature separation and merging ideas. To mitigate missed detections in small target scenarios and reduce noise impact, we incorporated the Coordinate Attention for Efficient Mobile Network Design (CA) module strategically. Additionally, we introduced a dynamic convolutional module to address misdetection and leakage issues stemming from significant target size variations, enhancing network robustness. An experimental validation was conducted on the FloW-Img sub-dataset provided by Okahublot. The results demonstrated that our enhanced YOLOv7 model outperforms the original network, exhibiting significant improvement in leakage reduction, with a mean Average Precision (mAP) of 81.1%. This represents a 5.2 percentage point enhancement over the baseline YOLOv7 model. In addition, the new model also has some advantages over the latest small-target-detection algorithms such as FCOS and VFNet in some respects.