A Lightweight Safety Helmet Detection Network Based on Bidirectional Connection Module and Polarized Self-attention

Safety helmets worn by construction workers in substations can reduce the accident rate in construction operations. With the mature development of smart grid and target detection technology, automatic monitoring of helmet wearing by using the cloud-side collaborative approach is of great significance in power construction safety management. However, existing target detectors have a large number of redundant calculations in the process of multi-scale feature fusion, resulting in additional computational overhead for the detectors. To solve this problem, we propose a lightweight target detection model PFBDet. First, we design cross-stage local bottleneck module FNCSP, and propose an efficient lightweight feature extraction network PFNet based on this combined with Polarized Self-Attention to optimize the computational complexity while obtaining more feature information. Secondly, to address the redundancy overhead brought by multi-scale feature fusion, we design BCM (bidirectional connection module) based on GSConv and lightweight upsampling operator CARAFE, and propose an efficient multi-scale feature fusion structure BCM-PAN based on this combined with single aggregation cross-layer network module. To verify the effectiveness of the method, we conducted extensive experiments on helmet image datasets such as Helmeted, Ele-hat and SHWD, and the experimental results show that the proposed method has better recognition accuracy with less computational effort. And it is higher than most high-performance target detectors, which can meet the real-time detection of construction personnel wearing helmets in the construction scenarios of power systems.