During the inspection and maintenance of electrical equipment at high altitudes, electrical operators may neglect wearing safety belts or wear them incorrectly, increasing the risk of falling accidents. This paper proposes a multi-scale feature fusion network (MFFNet) for detecting the wearing of aerial work safety belts. The network mainly consists of Cross Stage Partial and Fast (CSPF) module and Multi-Scale Feature Fusion (MFF) module. The CSPF module effectively extracts spatial features while reducing the transmission of redundant feature information. The MFF module allows the extraction of information from different scales of the backbone, enabling the fusion of multiple-scale features and enhancing their representation. Additionally, the GSConv module is utilized instead of standard convolution for downsampling purposes to reduce the number of parameters. And a detection layer is added to optimize the network's detection performance.
For several reasons, only part of the module code is exposed