YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union
The efficient detection and counting of pig populations is critical for the promotion of intelligent breeding. Traditional methods for pig detection and counting mainly rely on manual labor, which is either time-consuming and inefficient or lacks sufficient detection accuracy. To address these issue...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Animals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-2615/13/20/3201 |
| _version_ | 1827721972451115008 |
|---|---|
| author | Wangli Hao Li Zhang Meng Han Kai Zhang Fuzhong Li Guoqiang Yang Zhenyu Liu |
| author_facet | Wangli Hao Li Zhang Meng Han Kai Zhang Fuzhong Li Guoqiang Yang Zhenyu Liu |
| author_sort | Wangli Hao |
| collection | DOAJ |
| description | The efficient detection and counting of pig populations is critical for the promotion of intelligent breeding. Traditional methods for pig detection and counting mainly rely on manual labor, which is either time-consuming and inefficient or lacks sufficient detection accuracy. To address these issues, a novel model for pig detection and counting based on YOLOv5 enhanced with shuffle attention (SA) and Focal-CIoU (FC) is proposed in this paper, which we call YOLOv5-SA-FC. The SA attention module in this model enables multi-channel information fusion with almost no additional parameters, enhancing the richness and robustness of feature extraction. Furthermore, the Focal-CIoU localization loss helps to reduce the impact of sample imbalance on the detection results, improving the overall performance of the model. From the experimental results, the proposed YOLOv5-SA-FC model achieved a mean average precision (mAP) and count accuracy of 93.8% and 95.6%, outperforming other methods in terms of pig detection and counting by 10.2% and 15.8%, respectively. These findings verify the effectiveness of the proposed YOLOv5-SA-FC model for pig population detection and counting in the context of intelligent pig breeding. |
| first_indexed | 2024-03-10T21:30:23Z |
| format | Article |
| id | doaj.art-750a11b16553456f86f1dfe7dd8fc1d4 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-10T21:30:23Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-750a11b16553456f86f1dfe7dd8fc1d42023-11-19T15:24:34ZengMDPI AGAnimals2076-26152023-10-011320320110.3390/ani13203201YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over UnionWangli Hao0Li Zhang1Meng Han2Kai Zhang3Fuzhong Li4Guoqiang Yang5Zhenyu Liu6School of Software, Shanxi Agricultural University, Jingzhong 030801, ChinaSchool of Software, Shanxi Agricultural University, Jingzhong 030801, ChinaSchool of Software, Shanxi Agricultural University, Jingzhong 030801, ChinaSchool of Software, Shanxi Agricultural University, Jingzhong 030801, ChinaSchool of Software, Shanxi Agricultural University, Jingzhong 030801, ChinaSchool of Software, Shanxi Agricultural University, Jingzhong 030801, ChinaSchool of Software, Shanxi Agricultural University, Jingzhong 030801, ChinaThe efficient detection and counting of pig populations is critical for the promotion of intelligent breeding. Traditional methods for pig detection and counting mainly rely on manual labor, which is either time-consuming and inefficient or lacks sufficient detection accuracy. To address these issues, a novel model for pig detection and counting based on YOLOv5 enhanced with shuffle attention (SA) and Focal-CIoU (FC) is proposed in this paper, which we call YOLOv5-SA-FC. The SA attention module in this model enables multi-channel information fusion with almost no additional parameters, enhancing the richness and robustness of feature extraction. Furthermore, the Focal-CIoU localization loss helps to reduce the impact of sample imbalance on the detection results, improving the overall performance of the model. From the experimental results, the proposed YOLOv5-SA-FC model achieved a mean average precision (mAP) and count accuracy of 93.8% and 95.6%, outperforming other methods in terms of pig detection and counting by 10.2% and 15.8%, respectively. These findings verify the effectiveness of the proposed YOLOv5-SA-FC model for pig population detection and counting in the context of intelligent pig breeding.https://www.mdpi.com/2076-2615/13/20/3201pigdetectioncountingshuffle attentionfocal loss |
| spellingShingle | Wangli Hao Li Zhang Meng Han Kai Zhang Fuzhong Li Guoqiang Yang Zhenyu Liu YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union Animals pig detection counting shuffle attention focal loss |
| title | YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union |
| title_full | YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union |
| title_fullStr | YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union |
| title_full_unstemmed | YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union |
| title_short | YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union |
| title_sort | yolov5 sa fc a novel pig detection and counting method based on shuffle attention and focal complete intersection over union |
| topic | pig detection counting shuffle attention focal loss |
| url | https://www.mdpi.com/2076-2615/13/20/3201 |
| work_keys_str_mv | AT wanglihao yolov5safcanovelpigdetectionandcountingmethodbasedonshuffleattentionandfocalcompleteintersectionoverunion AT lizhang yolov5safcanovelpigdetectionandcountingmethodbasedonshuffleattentionandfocalcompleteintersectionoverunion AT menghan yolov5safcanovelpigdetectionandcountingmethodbasedonshuffleattentionandfocalcompleteintersectionoverunion AT kaizhang yolov5safcanovelpigdetectionandcountingmethodbasedonshuffleattentionandfocalcompleteintersectionoverunion AT fuzhongli yolov5safcanovelpigdetectionandcountingmethodbasedonshuffleattentionandfocalcompleteintersectionoverunion AT guoqiangyang yolov5safcanovelpigdetectionandcountingmethodbasedonshuffleattentionandfocalcompleteintersectionoverunion AT zhenyuliu yolov5safcanovelpigdetectionandcountingmethodbasedonshuffleattentionandfocalcompleteintersectionoverunion |