A Lightweight Object Detection Method for Bank Operation and Maintenance Scenarios

Abstract

Due to the complex background of bank operation and maintenance scenarios, it is extremely difficult to detect and analyze the object of operation and maintenance scenarios. The traditional target detection algorithms, such as F-RCNN, are not suitable for object detection. We propose a lightweight object detection method for bank operation and maintenance scenarios based on deep learning. The proposed method highlights two improvements based on Yolov5. The first adjustment of it is this method constructs a new convolution method and a new activation function. Also, we use the multi-scale output strategy of Feature Pyramid Networks (FPN) to develop a new lightweight feature extraction algorithm. These changes we made are based on the real-life situation, such as bank operation and maintenance scenarios. Experiments have been carried out on the same data set with multiple object detection algorithms, and the experimental results show that the proposed method reduces the complexity of the activation function. Meanwhile, the module is flexible, and can be easily applied to other similar business scenario detection models.