Abstract:
Objective Aiming at fast recognition of multi-feature variable target by robot vision in field environment, and considering the problem that the accuracy of target is affected by leaves, shade and light, a fast recognition method of multi-feature target was proposed.
Method A multi-scale feature extraction and classification model was proposed for three targets including banana fruit, fruit axis and flower bud. New target candidate box parameters were designed using clustering algorithm. The network structure parameters of YOLOv3 model were optimized and the YOLO-Banana model was proposed. In order to balance the speed and accuracy, YOLO-Banana and Faster R-CNN were used to conduct experiments on banana multi-targets with varying sizes. The effects of algorithms on recognition accuracy and speed were analyzed.
Result The average accuracies of YOLO-Banana and Faster R-CNN algorithms on banana fruit, fruit axis and flower bud were 91.03% and 95.16% respectively, average recognition time per photo was 0.237 and 0.434 s respectively. Therefore the accuracies of two algorithms were both above 90%. YOLO-Banana had relatively 1.83 times faster speed than Faster R-CNN, better satisfying the requirement of real-time operation.
Conclusion In the wild environment, utilization of YOLO-Banana model for banana multi-target recognition can meet the speed and accuracy requirements for visual recognition by the bud-breaking robots.
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