CT-YoloTrad: fast and accurate recognition of point-distributed coded targets for UAV images incorporating CT-YOLOv7

Abstract

Abstract Artificial point-distributed coded targets owns unique coded sequence numbers that can be recognized automatically. To address the issue of decreasing recognition accuracy and efficiency of existing recognition methods in complicated circumstances, an improved object detection model for coded target acquisition from unmanned aerial vehicle (UAV) images, CT-YOLOv7, is proposed. This improved model is based on the original YOLOv7 model, replacing several Conv with partial convolution (PConv), while introducing the bi-level routing attention mechanism, and designing the CBS-R structure and CBS-PR structure. In addition, the loss function is replaced with WIOU loss function to further improve the model’s performance. Based on the above, the new recognition method of point-distributed coded targets for UAV images is organized as follows. Firstly, CT-YOLOv7 is embedded into the front-end of the classical coded targets recognition process (that is, the coded targets are first extracted). Then, the extraction results are fed into the classical recognition algorithm for recognition. Lastly, the recognition results are inverse-calculated back to the original image. The new method aims to focus the processing on the region of interest to achieve fast and accurate coded targets recognition for UAV images. The experimental results show that CT-YOLOv7’s detection accuracy is 90.83%, which improves the accuracy by 8.46% and reduces the computation by 11.54% compared to the original YOLOv7. By incorporating the CT-YOLOv7 model, the time consumption for coded target recognition of a single UAV image is 150–350ms, which improves the average efficiency by 3–5 times compared with the classical method. Furthermore, the proposed method can correctly recognize regions with shadows and noise, and the recognition accuracy is improved by 15%–40%. With the method proposed in this paper, the coded targets are expected to be applied into UAV photogrammetry or remote sensing to realize accurate and quasi-real-time recognition.