Abstract Achieving the rapid and accurate detection of apple flowers in natural environments is essential for yield estimation and the development of an automatic flower thinner. A real-time apple flower detection method using the channel pruned YOLO v4 deep learning algorithm was proposed. First, the YOLO v4 model under the CSPDarknet53 framework was built, and then, to simplify the apple flower detection model and ensure the efficiency of the model, the channel pruning algorithm was used to prune the model. Finally, a total of 2230 manually labeled apple flower images (including three varieties of Fuji, Red Love, and Gala) were used to fine-tune the model to achieve the fast and accurate detection of apple flowers. The test results showed that the number of parameters of the apple flower detection model after pruning was reduced by 96.74%, the model size was reduced by 231.51 MB, the inference time was decreased by 39.47%, and the mAP was 97.31%, which was only 0.24% lower than the model before pruning. To verify the effectiveness of the proposed method, five different deep learning algorithms including the Faster R-CNN, Tiny-YOLO v2, YOLO v3, SSD 300 and EfficientDet-D0 were compared. The comparative results showed that the mAP of the apple flower detection using the proposed method was 97.31%; the detection speed was 72.33f/s; the model size was 12.46 MB; the mAP was 12.21%, 15.56%, 14.19%, 5.67% and 7.79% higher than the other five algorithms, respectively; and the detection speed could meet the real-time requirements. Furthermore, the detection performance of apple flowers under different species of apple trees and illumination conditions was discussed. The results indicated that the proposed method had strong robustness to the changes of fruit tree varieties and illumination directions. The results showed that it was feasible to apply the proposed method for the real-time and accurate detection of apple flowers. The research could provide technical references for orchard yield estimation and the development of apple flower thinning robots.

中文翻译：

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使用基于通道剪枝的YOLO v4深度学习算法在自然环境下实时准确检测苹果花

摘要 实现自然环境中苹果花的快速准确检测对于产量估算和开发自动花卉稀释器至关重要。提出了一种使用通道剪枝YOLO v4深度学习算法的实时苹果花检测方法。首先构建了CSPDarknet53框架下的YOLO v4模型，然后为了简化苹果花检测模型并保证模型的效率，采用通道剪枝算法对模型进行剪枝。最后，利用总共2230张人工标注的苹果花图像（包括富士、红爱、嘎啦三个品种）对模型进行微调，实现对苹果花的快速准确检测。测试结果表明，修剪后的苹果花检测模型的参数个数减少了96.74%，模型大小减少了 231.51 MB，推理时间减少了 39.47%，mAP 为 97.31%，仅比剪枝前的模型低 0.24%。为了验证所提出方法的有效性，比较了五种不同的深度学习算法，包括 Faster R-CNN、Tiny-YOLO v2、YOLO v3、SSD 300 和 EfficientDet-D0。对比结果表明，该方法检测苹果花的mAP为97.31%；检测速度为72.33f/s；模型大小为 12.46 MB；mAP分别比其他五种算法高12.21%、15.56%、14.19%、5.67%和7.79%；检测速度满足实时性要求。此外，还讨论了不同苹果树种和光照条件下苹果花的检测性能。结果表明，该方法对果树品种和光照方向的变化具有较强的鲁棒性。结果表明，将所提方法应用于苹果花的实时准确检测是可行的。该研究可为果园产量估算和苹果疏花机器人的开发提供技术参考。