Electric bicycle detection in elevator car based on YOLOv5
Zhenhai Wang, Chuanping Hu, Jing Li · 2023
Aiming at the problems that electric bicycles are prone to fire when charged at home, such as poor effectiveness of electric bicycle blocking systems in elevator cars, and low target detection accuracy, this paper proposes an improved YOLOv5 algorithm for detecting electric bicycles in elevator cars. Firstly, an FcaNet network model is embedded in the backbone network. The FcaNet network is implemented based on discrete cosine transform, mining multiple finite frequency domain components, making full use of input channel information, and improving the network's feature extraction ability; Secondly, in the process of feature fusion, relying on the mapping of CBAM attention mechanism to channel and spatial information, the CBAM attention mechanism is fused with the C3 model to form a C3CBAM model, which optimizes the fusion process of feature information, thereby enabling the model to achieve better detection performance; Finally, replacing the activation function in the SPP structure and using ReLU to replace SiLU improves the detection accuracy and speed of the algorithm. The experimental results show that the accuracy P, the recall rate R mAP Compared with the improved YOLOv5 algorithm, the accuracy of the algorithm was improved by 3.2 percentage points, and the recall rate was improved by 2.4 percentage points, mAP Increased by 4.1 percentage points, effectively improving the detection accuracy of the electric bicycle inside the elevator car.