Research on Image Recognition Based on Improved YOLOv5s Network Model Algorithm
- Resource Type
- Conference
- Authors
- Xie, Keying; Li, Haoyuan; Chen, Xiaodan; Zheng, Weichao; Gan, Jing; Chen, Xinxin
- Source
- 2024 4th International Conference on Neural Networks, Information and Communication (NNICE) Neural Networks, Information and Communication (NNICE), 2024 4th International Conference on. :658-662 Jan, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
YOLO
Training
Analytical models
Image recognition
Adaptive systems
Bidirectional control
Artificial neural networks
Deep learning
Object detection
YOLOv5s
Network structure
Image processing
- Language
In order to achieve rapid classification and extraction of pig target features. Propose an improved pig image object detection method based on YOLOv5s, using YOLOv5s adaptive anchor box calculation to obtain anchor boxes suitable for self built datasets; Combining convolutional block attention module to extract feature backbone network, improving model detection performance and reducing parameters. The Mosaic data augmentation method was used to enhance the image richness of the dataset and enhance the feature fusion ability of the network model. The improved algorithm was compared with the original YOLOv5s algorithm on a self-made dataset for ablation experiments. The results showed that the mean mAP of the improved algorithm reached 0.952, which showed a significant improvement in detection accuracy compared to other mainstream algorithms.