An improved YOLOX approach for low-light and small object detection: PPE on tunnel construction sites

Abstract

Tunnel construction sites pose a significant safety risk to workers due to the low-light conditions that can affect visibility and lead to accidents. Therefore, identifying personal protective equipment (PPE) is critical to prevent injuries and fatalities. A few research has addressed the challenges posed by tunnel construction sites whose light conditions are lower and images are captured from a distance. In this study, we proposed an improved YOLOX approach and a new dataset for detecting low-light and small PPE. We modified the YOLOX architecture by adding ConvNeXt modules to the backbone for deep feature extraction and introducing the fourth YOLOX head for enhancing multiscale prediction. Additionally, we adopted the CLAHE algorithm for augmenting low-light images after comparing it with eight other methods. Consequently, the improved YOLOX approach achieves an mAP of 86.94%, which is 4.23% higher than the original model and outperforms selected state-of-the-art. It also improves the AP of small object classes by 7.17% on average and attains a real-time processing speed of 22 FPS. Furthermore, we constructed a novel dataset with 8,285 low-light instances and 6,814 small ones. The improved YOLOX approach offers accurate and efficient detection performance, which can reduce safety incidents on tunnel construction sites.