Chemical Safety Sign Detection: A Survey and Benchmark

There is a high danger of accidents in chemical manufacturing plants. A devise that could automatically detect safety signs in the vicinity of a person could issue verbal warnings in order to reduce this risk. The most important task here is to correctly identify such signs from images. While there have been many achievements in the field of traffic sign detection, there is only very little research on detecting safety signs. In this work, we first provide an open and comprehensive benchmark dataset with 4650 images (expanded to 27900 images) of 30 chemical safety signs, each with a class label and bounding box and annotated with other image features. We then conduct a comprehensive analysis comparing the performance of the state-of-the-art deep learning models Faster R-CNN, SSD, YOLOv3-spp, and YOLOv5 on this dataset. In our study, YOLOv5 performs the best. It has the best mean average precision (98.9%), the best average recall (96.9%), and can process the highest number of images per second (71) on our hardware. It is already close to be sufficient for real-world application, but, like all investigated methods, suffers when detecting many signs at once, small signs, or signs in front of complex backgrounds. Our study closes an important gap in research and lays the foundation for solid future work in the domain of sign detection for improving worker safety.