KPE-YOLOv5: An Improved Small Target Detection Algorithm Based on YOLOv5

At present, the existing methods have many limitations in small target detection, such as low accuracy, a high rate of false detection, and missed detection. This paper proposes the KPE-YOLOv5 algorithm aiming to improve the ability of small target detection. The algorithm has three improvements bas...

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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 4; p. 817
Main Authors Yang, Rujin, Li, Wenfa, Shang, Xinna, Zhu, Deping, Man, Xunyu
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2023
Subjects
Accuracy
Algorithms
Boxes
Classification
Clustering
Computer networks
Datasets
Deep learning
Experiments
Feature extraction
Image processing
Machine learning
Methods
Neural networks
Object recognition (Computers)
Pattern recognition
Sensors
Target detection
China
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Summary:At present, the existing methods have many limitations in small target detection, such as low accuracy, a high rate of false detection, and missed detection. This paper proposes the KPE-YOLOv5 algorithm aiming to improve the ability of small target detection. The algorithm has three improvements based on the YOLOv5 algorithm. Firstly, it achieves more accurate size of anchor-boxes for small targets by K-means++ clustering technology. Secondly, the scSE (spatial and channel compression and excitation) attention module is integrated into the new algorithm to encourage the backbone network to pay greater attention to the feature information of small targets. Finally, the capability of small target feature extraction is improved by increasing the small target detection layer, which also increases the detection accuracy of small targets. We evaluate KPE-YOLOv5 on the VisDrone-2020 dataset and compare performance with YOLOv5. The results show that KPE-YOLOv5 improves the detection mAP by 5.3% and increases the P by 7%. The KPE-YOLOv5 algorithm has better detection outcome than YOLOv5 for small target detection.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12040817