Threat detection in transmission scenario based on TATLNet

Abstract

The operation of cranes and other large machinery threatens the safety of transmission lines. In order to solve this problem in the transmission scenario, the research from the aspects of data enhancement, network structure and the hyperparameters of the algorithm were performed. And a new end-to-end transmission line threat detection method based on TATLNet were proposed in this paper, which included the suspicious areas generation network VRGNet and threat discrimination network VTCNet. VRGNet and VTCNet share part of the convolution network for feature sharing and we used the model compression to compress the model volume and improved the detection efficiency. The method can realize accurate detection of large-scale machinery invading in the transmission scene from the perspective of computer vision and system engineering. To mend the insufficient training data, the data set was expanded by a combination of various data enhancement techniques. The sufficient experiments were carried out to explore the multiple hyperparameters of this method, and its optimal configuration was studied by synthesizing detection accuracy and inference speed. The research results are sufficient. With increase in the number of grids, the accuracy and recall first increase and then decrease, whereas, the detection efficiency decreases rapidly with increase in the number of grids. Considering the detection accuracy and reasoning speed, 9 × 9 is the optimal division strategy. With the increase in the input image resolution, the detection accuracy increases steadily and detection efficiency decreases gradually. To balance the detection accuracy and inference efficiency, 480 × 480 is selected as the final image input resolution. Experimental results and field deployment demonstrate that compared with other lightweight object detection algorithms, this method has better accuracy and efficiency in large-scale machinery invasion detection such as cranes in transmission fields, and meets the demands of practical applications.

Keywords

• deep learning
• threat detection
• feature sharing
• transmission scene
• lightweight network