Multi-scale receptive field grouped and split attention network for real-time detection of hazardous chemical gas leaks in infrared images

Abstract The petrochemical industry faces frequent hazardous gas leaks, which demand precise and timely detection to avert severe consequences. Existing computer vision approaches encounter challenges due to limitations in gas characteristics and scene features. To address these issues, we propose a multiscale receptive field grouped and split attention network, GAS-YOLO, that integrates infrared imaging technology. Within GAS-YOLO, we design a novel module, multi-scale receptive field grouped convolution (MRFGConv), to preserve fine-grained information, preventing detail loss and addressing spatial attention feature-sharing issues. An innovative split convolution attention (SCA) mechanism in the C2f module effectively couples multi-scale features, balancing performance and efficiency. Additionally, the asymptotic feature pyramid network (AFPN) facilitates the mutual interaction of information between non-adjacent levels, enabling advanced feature fusion. Using benchmark InfraGasLeakDataset, GAS-YOLO surpasses YOLOv8-n by 5.8% mAP50, with SCA outperforming state-of-the-art attention models. Experiment results validate the effectiveness and feasibility of our proposed approaches, providing valuable insights into hazardous chemical gas leak detection.