Detection method of a goat in a natural scene based on improved YOLOv4

Mingjuan Han; Ding Han; Xihao Wang

doi:10.1117/12.2643586

9 October 2022 Detection method of a goat in a natural scene based on improved YOLOv4

Mingjuan Han, Ding Han, Xihao Wang

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 122462B (2022) https://doi.org/10.1117/12.2643586
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

In order to realize the fast and accurate detection of Albas cashmere goat in natural scene, a recognition method of Albas cashmere goat in natural scene based on improved ylov4 is proposed. Attention mechanism can enhance the feature extraction ability of the network and enhance the performance of the model. After analyzing the structure of yolov4 special network, attention mechanism is added to the three positions of yolov4 model to further improve the feature extraction ability of the network. For the improved model training, P, R, F1 and mAP are selected as model performance indicators. Experiments on the data set show that the accuracy rate of the improved yolov4 model is 85.83%, the recall rate is 85.92%, F1 is 85.87%, and mAP is 82.32%. Among them, the recall rate of the improved yolov4 model is 1.5% higher than that of yolov3, the average accuracy is 1.53% higher than that of the original yolov4 network, 3.31% higher than that of yolov3 network and 5.2% higher than that of yolov3-cbam. To some extent, this model can realize the rapid and accurate detection of Albas cashmere goat, which provides technical support for the development of intelligent animal husbandry.

Citation Download Citation

Mingjuan Han, Ding Han, and Xihao Wang "Detection method of a goat in a natural scene based on improved YOLOv4", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 122462B (9 October 2022); https://doi.org/10.1117/12.2643586

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KEYWORDS

Target detection

Data modeling

Feature extraction

Performance modeling

Detection and tracking algorithms

Convolution

Convolutional neural networks

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