A Counting Method of Red Jujube Based on Improved YOLOv5s
A Counting Method of Red Jujube Based on Improved YOLOv5s
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DOI：
10.3390/agriculture12122071
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发表时间：
2022-12-01
影响因子：
3.6
通讯作者：
Guo, Jiapan
中科院分区：
文献类型：
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作者：
Qiao, Yichen;Hu, Yaohua;Guo, Jiapan
由于光照、叶果间遮荫、果间遮荫等复杂的环境因素，果园中红蜘蛛的快速识别和计数是一项具有挑战性的任务。提出了一种基于改进YOLOv5s的红枣计数方法，实现了红枣的快速准确检测，减小了模型规模和估计误差。采用ShuffleNet V2作为模型的主干，提高模型检测能力，减轻模型的权重。此外，提出了一种新的数据加载模块Stem，以防止由于特征图大小的变化而导致的信息丢失。用BiFPN代替PANet，增强模型特征融合能力，提高模型精度。最后，利用改进的YOLOv5s检测模型对红蜘蛛进行计数。实验结果表明，改进模型的整体性能优于YOLOv5s。与YOLOv5s相比，改进后的模型在模型参数数量和模型大小方面分别为原网络的6.25%和8.33%，精确度、召回率、F1得分、AP和Fps分别提高了4.3%、2.0%、3.1%、0.6%和3.6%。此外，RMSE和MAPE分别下降了20.87%和5.18%。因此，改进后的模型在内存占用和识别准确率方面具有优势，为红枣产量的视觉估测提供了依据。
Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision.