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

Abstract

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 researches have 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 a mean average precision of 86.94%, which is 4.23% higher than the original model and outperforms selected state-of-the-art. It also improves the average precision of small object classes by 7.17% on average and attains a real-time processing speed of 22 FPS (Frames Per Second). Furthermore, we constructed a novel dataset with 8285 low-light instances and 6814 small ones. The improved YOLOX approach offers accurate and efficient detection performance, which can reduce safety incidents on tunnel construction sites.