GSD-YOLOX: Lightweight and more accurate object detection models

Abstract

We present an enhanced YOLOX model for vehicle detection, addressing issues of slow detection speed, high parameter counts, and complex computations. Our model significantly improves inference speed while maintaining high detection accuracy. We introduce two lightweight modules, DG and DS, to reduce model size and enhance detection speed. The DG module eliminates redundant features during feature extraction, and the DS module optimizes the performance of DG, enhancing feature extraction efficiency. We utilize the CIoU loss function for accurate bounding box regression. Additionally, we introduce the Chinese Chongqing Vehicle Detection Benchmark (CCVTDB) dataset to address dataset limitations. Our lightweight model achieves an impressive 83.36% detection accuracy on CCVTDB at 65 FPS with 4.4 million parameters. Compared to the original model, our approach improves detection speed by 30% and reduces model size by 51%, while maintaining substantial detection performance.