A detection algorithm for cherry fruits based on the improved YOLO-v4 model
A detection algorithm for cherry fruits based on the improved YOLO-v4 model
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DOI：
10.1007/s00521-021-06029-z
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发表时间：
2021-05-26
影响因子：
6
通讯作者：
Yuan, Hai
中科院分区：
文献类型：
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作者：
Gai, Rongli;Chen, Na;Yuan, Hai
“数字化”农业正在迅速影响农业产值。机器人采摘成熟农产品，实现精准快速采摘，让农业收获智能化。如何提高产品产量，也成为数字农业面临的挑战。在樱桃生长过程中，实现对樱桃果实的快速、准确检测是樱桃果实数字农业发展的关键。由于樱桃果实检测不准确，遮荫等环境问题成为樱桃果实检测的最大挑战。本文提出了一种改进的YOLO-V4深度学习算法来检测樱桃果实。此型号适用于体积较小的樱桃水果。提出在YOLO-V4骨干网络CSPDarknet 53网络的基础上增加网络，结合DenseNet将层间密度，即YOLO-V4模型中的先验框，改为符合樱桃果实形状的圆形标记框。在改进的YOLO-V4模型基础上，加强了特征提取，深化了网络结构，提高了检测速度。为了验证该方法的有效性，比较了YOLO-V3，YOLO-V3-dense和YOLO-V4的不同深度学习算法。结果表明，本文改进的YOLO-V4模型（YOLO-V4-dense）网络获得的mAP（平均精度）值比yolov 4提高了0.15。在实际果园应用中，可以检测出同一地区樱桃成熟度不同的樱桃，对成熟度差异较大的果实进行人工干预，最终提高樱桃果实的产量。
"Digital" agriculture is rapidly affecting the value of agricultural output. Robotic picking of the ripe agricultural product enables accurate and rapid picking, making agricultural harvesting intelligent. How to increase product output has also become a challenge for digital agriculture. During the cherry growth process, realizing the rapid and accurate detection of cherry fruits is the key to the development of cherry fruits in digital agriculture. Due to the inaccurate detection of cherry fruits, environmental problems such as shading have become the biggest challenge for cherry fruit detection. This paper proposes an improved YOLO-V4 deep learning algorithm to detect cherry fruits. This model is suitable for cherry fruits with a small volume. It is proposed to increase the network based on the YOLO-V4 backbone network CSPDarknet53 network, combined with DenseNet The density between layers, the a priori box in the YOLO-V4 model, is changed to a circular marker box that fits the shape of the cherry fruit. Based on the improved YOLO-V4 model, the feature extraction is enhanced, the network structure is deepened, and the detection speed is improved. To verify the effectiveness of this method, different deep learning algorithms of YOLO-V3, YOLO-V3-dense and YOLO-V4 are compared. The results show that the mAP (average accuracy) value obtained by using the improved YOLO-V4 model (YOLO-V4-dense) network in this paper is 0.15 higher than that of yolov4. In actual orchard applications, cherries with different ripeness of cherries in the same area can be detected, and the fruits with larger ripeness differences can be artificially intervened, and finally, the yield of cherry fruits can be increased.