An Improved ResNet50 for Environment Image Classification
Jiacheng Wan, Bingchan Li, Kun Wang, Xixi Teng, Tao Wang, Bo Mao
Abstract
With the drastic changes in the ecological environment, the issue of blue-green algae blooms has become increasingly common, significantly affecting human habitats. Real-time image data extraction and classification via deep learning models play a critical role in detecting blue-green algae in water bodies and facilitating subsequent alerts and interventions. However, current research in this field faces several challenges. Firstly, traditional convolutional methods struggle to effectively extract features such as textures and outlines from images. Secondly, the target and background areas in captured images often share similar environments, leading to classification errors. Addressing these issues, this paper initially enhances the feature representation capabilities of the ResNet50 architecture by integrating a detail feature extraction module. Subsequently, a binary mask image fusion method is proposed to direct the model’s focus towards learning features of water regions. Our constructed model shows improved classification performance on real-time camera data compared to other classification models.