Research on UAV Detection of Threat Target around oil Pipeline Based on Deep Learning

Abstract

With the development of UAV, UAV has been applied to various projects with its advantages of low construction cost,low safety risk coefficient and convenient operation.In terms of UAV platform, currently composite wing and multi-rotor UAV are typically adopted, which can realize basic flight route. In terms of image detection, neural network is mainly used to classify and recognize the target in the image. In this paper, the YOLOV4 algorithm is improved to make it more suitable for UAV detection of ground targets.In the ground detection of UAV, most of them are small targets, so clustering method is used to redesign anchor for small targets. Because the features of small targets have more details in the shallow feature layer, the shallow feature is superimposed into the feature extraction layer, and the shallow feature and the deep feature are fused.In the data processing, data enhancement, color dithering, flipping, cutting of the data set for expansion. Through the test of the modified network, the following results are obtained: the overall mAP is improved by 9.3%, the detection mAP for small targets such as people is improved by 23.75%, and the detection mAP for working vehicles is improved by 15.4%. The detection efficiency of small targets is improved, and the speed can meet the real-time requirements, and it can be deployed in the UAV for UAV detection.