基于改进YOLO-V3网络的百香果实时检测

doi:10.16088/j.issn.1001-6600.2020.06.004

摘要/Abstract

摘要： 针对目前流行的目标检测模型对真实果园中百香果检测的抗干扰能力不理想问题,本文提出基于改进的YOLO-V3网络对真实果园中百香果进行实时检测。首先,剔除YOLO-V3模型的大物体预测尺度,将3尺度预测降为2尺度预测,用于加快物体的检测速度;其次,在中型物体预测尺度后添加DenseNet网络,用于增强网络特征传播,提高模型的检测精度;最后,利用改进的YOLO-V3网络对百香果数据集进行多次训练,得到最优预训练模型。实验结果表明:改进的YOLO-V3网络实时检测效果好,对目标的平均检测精度高达97.5%以上,并且检测速度达到38幅/s,为实时检测百香果提供了有效方法。

关键词: 深度学习, 改进的YOLO-V3, 实时检测, DenseNet网络, 百香果

Abstract: Aiming at the problem that the anti-interference ability of the traditional deep learning method and the popular target detection model for the passion fruit detection in real orchards are not ideal, this paper proposes a real-time detection of passion fruit in real orchards based on the improved YOLO-V3 network. Firstly, the prediction scale of large objects in the YOLO-V3 model is eliminated, and the 3-scale prediction is reduced to 2-scale prediction to speed up the detection time. Secondly, the DenseNet network is added to the medium object prediction scale to enhance the propagation of network features for improving the detection accuracy of the model. Finally, the improved YOLO-V3 network is used to train the passion fruit dataset several times to obtain the optimal pre-training model.The experimental results show that the improved YOLO-V3 network has a good real-time detection effect.The average detection accuracy of the target is more than 97.5% and the detection speed is 38 frames/s. The new method provides an effective theoretical basis for real-time detection of passion fruit.

Key words: deep learning, improved YOLO-V3 network, real-time detection, DenseNet network, passion fruit

中图分类号:

TP391.4

唐熔钗, 伍锡如. 基于改进YOLO-V3网络的百香果实时检测[J]. 广西师范大学学报（自然科学版）, 2020, 38(6): 32-39.

TANG Rongchai, WU Xiru. Real-time Detection of Passion Fruit Based on Improved YOLO-V3 Network[J]. Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(6): 32-39.

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