<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Object detection is the use of computer to find out all the objects of interest in the image, determine their categories and locations, is one of the core problems in the field of computer vision. Traditional animal image target detection usually adopts the sliding window method, but due to the different sizes of the input images, this method has some problems such as insufficient training samples, low detection accuracy and slow speed. In order to solve such problems, based on the development of deep learning in recent years, this paper proposes an object detection algorithm based on convolutional neural network. YOLOv5 was used to effectively distinguish, identify and mark animal categories, which accelerated the training of the model and greatly improved the accuracy of target detection. Through the analysis of experimental data, it was concluded that the algorithm studied in this paper had good performance and good target detection results. Finally, the key problems of object detection research are summarized, and the future development trend of this field is prospected. When the number of training rounds is 30, the accuracy rate has reached about 70%, and after 50 rounds of training, some accuracy can reach 90%, which is excellent and better than other traditional algorithms.</p>
</abstract>

Object detection is the use of computer to find out all the objects of interest in the image, determine their categories and locations, is one of the core problems in the field of computer vision. Traditional animal image target detection usually adopts the sliding window method, but due to the different sizes of the input images, this method has some problems such as insufficient training samples, low detection accuracy and slow speed. In order to solve such problems, based on the development of deep learning in recent years, this paper proposes an object detection algorithm based on convolutional neural network. YOLOv5 was used to effectively distinguish, identify and mark animal categories, which accelerated the training of the model and greatly improved the accuracy of target detection. Through the analysis of experimental data, it was concluded that the algorithm studied in this paper had good performance and good target detection results. Finally, the key problems of object detection research are summarized, and the future development trend of this field is prospected. When the number of training rounds is 30, the accuracy rate has reached about 70%, and after 50 rounds of training, some accuracy can reach 90%, which is excellent and better than other traditional algorithms.

Research on Object Detection in Animal Images Based on Convolutional Neural Networks

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Object detection is the use of computer to find out all the objects of interest in the image, determine their categories and locations, is one of the core...

Epoch	Recall	mAP50	mAP50:95
3	0.062	0.009	0.015
10	0.259	0.220	0.119
30	0.507	0.520	0.248
50	0.753	0.739	0.350

Research on Object Detection in Animal Images Based on Convolutional Neural Networks

Online veröffentlicht: 16. März 2024

Seitenbereich: 45 - 54

DOI: https://doi.org/10.2478/ijanmc-2023-0076

Schlüsselwörter
Deep Learning, Object Detection, YOLO Algorithm, Animal Recognition

© 2023 Yuxin Niu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

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Different rounds of training

Research on Object Detection in Animal Images Based on Convolutional Neural Networks

Online veröffentlicht: 16. März 2024

Seitenbereich: 45 - 54

DOI: https://doi.org/10.2478/ijanmc-2023-0076

SchlüsselwörterDeep Learning, Object Detection, YOLO Algorithm, Animal Recognition

© 2023 Yuxin Niu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

Figure 12.

Different rounds of training

Schlüsselwörter
Deep Learning, Object Detection, YOLO Algorithm, Animal Recognition