Open AccessAn Improved Algorithm for Target Detection in Low Light Conditions
Author(s) -
Dong Yin,
Wengsheng Tang,
Peng Chen,
Bo Yang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2203/1/012045
Subject(s) - brightness , computer science , residual , artificial intelligence , computer vision , algorithm , image (mathematics) , pattern recognition (psychology) , optics , physics
The accuracy of pig target detection is not ideal due to insufficient light in a real pig farm environment. An improved enhanced network helps to increase the accuracy for pig target detection. The ResNet-Attention-RetinexNet algorithm (RA-RetinexNet) is proposed to solve the problems that YOLO V4 has low accuracy in pig image detection under low light and Mosaic data enhancement method cannot improve the brightness of low light image. In this model, we build residual connection and add attention mechanism, decreasing data loss of RetinexNet and enhancing the brightness information of images. The experimental results show that the model achieves better performance for pig target detection under low light.