1. 基于改进YOLOv5s的不同成熟度苹果目标检测方法.
- Author
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王勇, 陶兆胜, 石鑫宇, and 伍毅
- Abstract
[Objectives]This paper aimed to solve the problem of low target detection accuracy of apples with different ripeness under natural environment. [Methods]An improved YOLOv5s model SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block)was proposed for different ripeness apple detection. Firstly, the multi-scale target detection layer of YOLOv5s was improved, and the detection head of 160×160 feature map was constructed in Prediction to improve the detection accuracy of small-size apples with different ripeness. Secondly, Swin Transformer Block was integrated into the Backbone structure to strengthen the texture feature fusion of apples at the same level of ripeness, weaken the negative impact of the difference in texture feature distribution, and improve the generalization ability of the model. Finally, the Conv module of Neck structure was replaced by the dynamic convolution module ODConv, and the local feature mapping was refined to realize the full extraction of local apple fine-grained features. Experiments were carried out based on different ripeness apple data sets to verify the performance of the improved model. [Results]The precision, recall and average precision of the improved model SODSTR-YOLOv5s were 89.1%, 95.5% and 93.6%, respectively. The average precision of high, medium and low ripeness apples was 94.1%, 93.1% and 93.7%, respectively. The average detection time was 16 ms and the number of parameters was 7.34 M. Compared with the YOLOv5s model, the precision, recall and average precision of the improved model SODSTR-YOLOv5s increased by 3.8%, 5.0% and 2.9%, respectively, and the number of parameters and average detection time increased by 0.32 M and 5 ms, respectively. [Conclusions]The improved model SODSTR-YOLOv5s improved the detection ability of apples with different ripeness under natural environment. It can better meet the detection requirements of actual apple picking. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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