Object detection and localization algorithm in agricultural scenes based on YOLOv5

Jiachen Yang, Mengqi Han, Jingyi He, Jiabao Wen, Desheng Chen, Yibo Wang

Abstract: Object detection and target localization are important technologies in image processing and computer vision, which have a wide range of applications in agricultural systems. By fusing the YOLOv5 algorithm and monocular vision-based method, a target localization algorithm is proposed to accurately identify and locate the various objects in agricultural scenes. The GPS information of the target object can be obtained by means of processing the attitude angle information of the unmanned aerial vehicle (UAV) at the time when it takes pictures. The superiority of the proposed method is demonstrated through the test on the agricultural scenario dataset taken by UAV, with the experimental results showing the algorithm’s satisfactory speed and accuracy.