Research on reflective clothing recognition algorithm based on combining omni-dimensional dynamic convolution and partial convolution

Wenbi Ma, Zheng Guan, Xue Wang, Zhuqing Zhang, Jinde Cao

Abstract: Currently, in construction sites, road maintenance, airports, and other special scenarios, the process of checking whether workers are wearing reflective clothing for safety is overly reliant on manual operations, and this manual screening method is not only inefficient but also has huge labor costs. To address this problem, this paper proposes a new method for reflective clothing wear recognition. Firstly, by replacing some traditional convolutions in the neck network of the YOLOv7-tiny(You Only Look Once vertion 7 - tiny) algorithm with the ODConv(Omni-dimensional Dynamic Convolution) module, the four dimensions of the kernel space can be endowed with convolutional dynamics attributes, which improves the detection accuracy of the model. Secondly, the PConv(Partial Convolution) module is used to replace some other traditional convolutions in the neck network, aiming to ensure detection accuracy while reducing computational redundancy and memory access. Then, a new SPPC(Spatial Pyramid Pooling Curtail) module is proposed and replaces the SPPCSPC(Spatial Pyramid Pooling Cross Stage Partial Concat) module of the original neck network, which guarantees accuracy and reduces the number of model parameters at the same time. Finally, the algorithm model proposed in this paper is ported to the Jetson Nano edge computing device, which can well meet the demand for real-time detection of reflective clothing and lay the foundation for subsequent practical applications.