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| An environmental fruit detection method for light weight orchard
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| SHANG Gaogao， JIANG Kun， HAN Jiangyi， NI Wanlei |
| School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang， Jiangsu 212013, China |
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Abstract Rapid and accurate detection of citrus in orchard environment is the key for autonomous picking robot. To solve the problems of excessive redundancy and imbalanced detection speed with accuracy of existing models, the light weight environmental fruit detection method was proposed. Based on YOLOv4, the Focal Loss was introduced to improve the negative sample mining ability of the model in binary classification detection task, and the optimized model pruning method was proposed to solve the problem of model parameter redundancy. The experimental results show that by the proposed method，the MAP of citrus fruit data set in orchard environment reaches 94.22%, which is improved by 1.18% compared with YOLOv4 model.The model parameters are reduced by 95.22%, and the model size is 4.84% of the original size with the detection speed increased by 4.03 times.
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Received: 16 January 2022
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