Detection of piggery cleaning target under low-light condition

doi:10.3969/j.issn.1004-1524.20221447

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (9): 2240-2249.DOI: 10.3969/j.issn.1004-1524.20221447

• Biosystems Engineering • Previous Articles Next Articles

Detection of piggery cleaning target under low-light condition

LI Qi(), LI Yuzhe()

College of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, China

Received:2022-10-10 Online:2023-09-25 Published:2023-10-09
Contact: LI Yuzhe

Abstract

Abstract:

The cleaning of piggery without manual intervention can effectively prevent pigs from contracting diseases. Aiming at the problems of weak light, inconspicuous features, and mutual occlusion in the piggery scene, the low detection accuracy of cleaning targets, an improved YOLOv5 algorithm was proposed. In the preconditioning stage, the Retinex algorithm based on bilateral filtering was used to improve the detection ability of the piggery cleaning target under low light conditions; In the cleaning target detection process, the CBAM (convolutional block attention module) attention mechanism was introduced into the backbone of the YOLOv5 network to enable the network to learn more effective feature information of the cleaning target in the piggery; The frame regression loss function in the network was improved, and the DIoU-NMS algorithm was used to filter out the candidate frame of the cleaning target in the piggery, which improved the detection accuracy of the cleaning target in the case of partial occlusion. The experimental results showed that the improved YOLOv5 target detection algorithm improved the accuracy rate by 7.3 percentage point, the recall rate by 7.6 percentage point, and the average accuracy by 7.1 percentage point compared with the benchmark algorithm on the test set of piggery cleaning targets. The robustness was higher in the case of target occlusion. The result provided a basis for the research and development of animal husbandry intelligent cleaning equipment.

Key words: piggery, cleaning target, low-light conditions, attention mechanism, YOLOv5 algorithm

CLC Number:

S237

LI Qi, LI Yuzhe. Detection of piggery cleaning target under low-light condition[J]. Acta Agriculturae Zhejiangensis, 2023, 35(9): 2240-2249.

Figures/Tables 11

Fig.1 The structure of feature pyramide network (FPN)+pyramid attention network (PAN)

Fig.2 Piggery cleaning target

Fig.3 Algorithm framework of the improved YOLOv5 network Conv, Convolution; BN, Batch normalization; CBL, Convolution block; CSPNet, Cross stage partial networks; SPP, Spatial pyramid pooling; Concat, Concatenates tensors; Leaky relu, Activation function; Res unit, Residual component; CBAM, Convolutional block attention module.

Fig.4 Retinex piggery image enhancement algorithm based on bilateral filtering

Fig.5 Low-light image enhancement in the piggery a, Before image enhancement; b, After image enhancement.

Fig.6 The structure of CBMA model

Fig.7 Loss function change curve of model

Table 1 Ablation experiments results

序号 Serial number	图像增强 Image enhancement	加入CBAM Input CBAM	修改损失函数 Modify loss function	mAP/%
1	×	×	×	89.7
2	√	×	×	90.9
3	×	√	×	93.7
4	×	×	√	91.5
5	√	√	×	94.9
6	√	×	√	91.7
7	×	√	√	95.8
8	√	√	√	96.8

Table 2 Comparison of piggy cleaning target detection results with different algorithms

算法	P/%	R/%	mAP/%	FPS
Algorithm
Faster R-CNN	92.0	89.8	94.1	13
SSD	84.3	78.6	82.9	28
YOLOv5	89.1	84.2	89.7	30
Improved YOLOv5	96.4	91.8	96.8	30

Fig.8 Target test results before and after pretreatment

Fig.9 Clean target detection results for different algorithms a, No occlusion; b, Partial occlusion and complex background; c, Partial occlusion and simple background.

References 13

[1]	黄静, 张健. 一种基于改进SSD网络的猪个体目标检测方法研究[J]. 软件工程, 2022, 25(8): 25-29.
	HUANG J, ZHANG J. Research on a target detection algorithm of individual pig based on improved SSD network[J]. Software Engineering, 2022, 25(8): 25-29. (in Chinese with English abstract)
[2]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. New York: ACM, 2014: 580-587.
[3]	HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[4]	GIRSHICK R. Fast R-CNN[C]// 2015 IEEE International Conference on Computer Vision (ICCV). December 07-13, 2015, Santiago, Chile. IEEE, 2016: 1440-1448.
[5]	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[6]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016: 779-788.
[7]	蔡舒平, 孙仲鸣, 刘慧, 等. 基于改进型YOLOv4的果园障碍物实时检测方法[J]. 农业工程学报, 2021, 37(2): 36-43.
	CAI S P, SUN Z M, LIU H, et al. Real-time detection methodology for obstacles in orchards using improved YOLOv4[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(2): 36-43. (in Chinese with English abstract)
[8]	陈道怀, 汪杭军. 基于改进YOLOv4的林业害虫检测[J]. 浙江农业学报, 2022, 34(6): 1306-1315.
	CHEN D H, WANG H J. Detection of forest pests based on improved YOLOv4[J]. Acta Agriculturae Zhejiangensis, 2022, 34(6): 1306-1315. (in Chinese with English abstract)
[9]	LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[M]//Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 21-37.
[10]	WEI C, WANG W J, YANG W H, et al. Deep retinex decomposition for low-light enhancement[EB/OL]. 2018: arXiv: 1808.04560. https://arxiv.org/abs/1808.04560.
[11]	WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[M]// Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 3-19.
[12]	陈世权, 王从庆, 周勇军. 一种基于YOLOv5s和图像融合的行人检测方法[J]. 电光与控制, 2022, 29(7): 96-101.
	CHEN S Q, WANG C Q, ZHOU Y J. A pedestrian detection method based on YOLOv5s and image fusion[J]. Electronics Optics & Control, 2022, 29(7): 96-101. (in Chinese with English abstract)
[13]	王杰, 陈黎卿, 黄莉莉, 等. 基于Retinex的弱光条件下车道线识别方法[J]. 计算机与数字工程, 2019, 47(2): 451-456.
	WANG J, CHEN L Q, HUANG L L, et al. Lane recognition method in weak light condition based on retinex[J]. Computer & Digital Engineering, 2019, 47(2): 451-456. (in Chinese with English abstract)

Detection of piggery cleaning target under low-light condition

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