AdaHC: Adaptive hedge horizontal cross-section center detection algorithm

• A novel circle detection algorithm is presented. • The algorithm is based on salient color space, adaptive analysis channel, adaptive threshold interval, image enhancement and a priori data verification. • The algorithm can also work in complex environment and fully meet the needs of hedge pruning industry. • The proposed algorithm outweigh other algorithm. Finding the center point of the hedge is the key to automated pruning. In a complex outdoor environment, since the horizontal cross-section of the hedge is not a regular circle, and has many approximate circular contours, the performance of mainstream circle detection algorithms is not ideal. To this end, we propose an adaptive hedge horizontal cross-section center detection algorithm, named AdaHC , which can obtain the horizontal cross-section center coordinates of the hedge in real time by inputting the top view image of the hedge. The experimental results show that our proposed algorithm is significantly better than other circle detection algorithms. Its recognition accuracy can reach 100%, the average accuracy is over 80% (corresponding to an accuracy of 1 cm), and the average time consumption is 11.6 ms, be robust to the variations in light, hedge color, and background, fully meets the industrial requirements, and lays a good foundation for automatic hedge trimming.