自然环境下改进YOLOv5对小目标苹果的检测

doi:10.16182/j.issn1004731x.joss.24-0260

摘要/Abstract

摘要：

针对苹果的分布通常会存在遮挡、小目标，以及密集目标等问题，提出了一种改进YOLOv5的目标检测算法。在YOLOv5的基础上加入了坐标注意力机制、感受野模块，以及自适应空间特征融合，加强了对小目标检测的能力。将YOLOv5中使用的CIoU替换为了SIoU，提高了目标检测框的位置预测精度。将部分普通卷积替换为了深度可分离卷积，减少了计算量。实验结果表明：改进YOLOv5的综合性能要优于原始YOLOv5及其他算法， mAP值相比原始YOLOv5提升了9.6%。

关键词: 智能农业, 坐标注意力机制, 感受野, 自适应空间特征融合, 小目标检测, YOLOv5

Abstract:

The distribution of apples usually features occlusion and small and dense targets. To address these issues, a target detection algorithm was proposed based on an improved YOLOv5 model. Specifically, this paper added the coordinate attention (CA) mechanism, receptive field block (RFB), and adaptively spatial feature fusion (ASFF) modules to the YOLOv5, enhancing the ability to detect small targets. Additionally, the proposed algorithm replaced the CIoU in YOLOv5 with SIoU to improve the target detection box's prediction accuracy. Finally, some normal convolutions were replaced with depthwise separable convolutions (DSC), effectively reducing the calculation burden. Experiment results show that the comprehensive performance of the improved YOLOv5 is better than the original YOLOv5 and other algorithms. Moreover, its mAP value is improved by 9.6% in comparison with the original YOLOv5.

Key words: intelligent agriculture, coordinate attention mechanism, receptive field block, adaptively spatial feature fusion, small target detection, YOLOv5

中图分类号:

TP391

刘子龙,张磊 . 自然环境下改进YOLOv5对小目标苹果的检测[J]. 系统仿真学报, 2025, 37(8): 2124-2138.

Liu Zilong,Zhang Lei . Detection of Small Apple Targets Based on Improved YOLOv5 in Natural Environments[J]. Journal of System Simulation, 2025, 37(8): 2124-2138.

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DSC数量	参数量	P/%	R/%	mAP0.5/%
8	13 395 370	91.4	82.9	90.3
9	13 387 281	92.0	83.4	90.6
10	13 256 465	91.7	82.8	90.2
11	12 732 689	92.3	83.8	91.0
12	12 471 057	87.8	77.0	85.6
13	11 423 505	86.7	75.7	84.4
14	11 260 433	86.1	75.0	83.5

模型	P	R	mAP0.5
YOLOv5	84.6	72.2	81.4
改进YOLOv5	92.3	83.8	91.0

CA	RFB	ASFF	SIoU	P	R	mAP0.5
				84.6	72.2	81.4
√				83.8	72.7	81.4
	√			89.4	79.6	87.1
		√		89.0	78.8	86.9
			√	84.2	72.8	81.4
√	√	√	√	92.3	83.8	91.0

模型	P	R	mAP0.5
Faster RCNN	57.83	54.75	48.22
SSD	77.78	8.82	31.70
Efficientdet	97.77	11.45	15.92
YOLOv4	81.60	67.75	74.96
YOLOv5	84.60	72.20	81.40
改进YOLOv5	92.30	83.80	91.00