Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and Method
This work addresses safety monitoring in construction by providing a dataset and method for detection, though it is incremental as it builds on existing attention mechanisms.
The authors tackled the problem of detecting safety clothing and helmets for construction worker safety by constructing a large, realistic dataset (SFCHD) with 12,373 images and 50,552 annotations, and they developed a plug-and-play SCALE module that improved detector performance under low-light conditions.
Detecting safety clothing and helmets is paramount for ensuring the safety of construction workers. However, the development of deep learning models in this domain has been impeded by the scarcity of high-quality datasets. In this study, we construct a large, complex, and realistic safety clothing and helmet detection (SFCHD) dataset. SFCHD is derived from two authentic chemical plants, comprising 12,373 images, 7 categories, and 50,552 annotations. We partition the SFCHD dataset into training and testing sets with a ratio of 4:1 and validate its utility by applying several classic object detection algorithms. Furthermore, drawing inspiration from spatial and channel attention mechanisms, we design a spatial and channel attention-based low-light enhancement (SCALE) module. SCALE is a plug-and-play component with a high degree of flexibility. Extensive evaluations of the SCALE module on both the ExDark and SFCHD datasets have empirically demonstrated its efficacy in enhancing the performance of detectors under low-light conditions. The dataset and code are publicly available at https://github.com/lijfrank-open/SFCHD-SCALE.