Water Surface Target Detection Based on Improved YOLOv3 in UAV Images

Abstract

In order to better manage and protect rivers and lakes, the most important requirement is to find the objects on the surface of rivers and lakes in time. Generally, image segmentation and target detection are used to detect water surface targets. The former is sensitive to the selection of target features, with poor generalization ability and slow detection speed. The latter has not yet been applied to surface target detection in UAV images. In view of this situation, this paper proposes a target detection model based on YOLOv3, which is used to detect surface targets in UAV images. In order to verify the performance of the model, the images collected in this paper include five types of surface targets. These images are then enhanced by rotation transform, brightness transform and mirror transform, and the enhanced images are used to generate data sets. In the YOLOv3 model, we use the inception module for multi-scale depth features to process the deep features of the network. The module can activate the multi-scale sensing field of the deep features, so as to fully utilize the deep features and improve the detection accuracy of small and medium targets in the UAV image. In addition, we optimize the loss function to train the network better. The experimental results show that the mAP of the proposed Yolov3-inception is 81%, the detection speed is 23 frames per second, and the overall performance is better than YOLOv3, Faster RCNN and SSD. Therefore, this method is suitable for surface target detection in UAV images.