Optimizing Backbone Networks Through Hybrid–Modal Fusion: A New Strategy for Waste Classification

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 10; p. 3241
• Main Authors: Zhou, Houkui, Ding, Qifeng, Chen, Chang, Liao, Qinqin, Wang, Qun, Yu, Huimin, Hu, Haoji, Zhang, Guangqun, Hu, Junguo, He, Tao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21.05.2025; MDPI
• Subjects: Accuracy; Artificial intelligence; Automation; Classification; Composite materials; Costs; Datasets; Deep learning; Design; Efficiency; Hazardous materials; image recognition; Machine learning; model fusion; Neural networks; optimization strategy; Recycling; Telecommunication systems; transformer; waste classification; Waste management; New Jersey; transformer; waste classification; model fusion; optimization strategy; image recognition

With rapid urbanization, effective waste classification is a critical challenge. Traditional manual methods are time-consuming, labor-intensive, costly, and error-prone, resulting in reduced accuracy. Deep learning has revolutionized this field. Convolutional neural networks such as VGG and ResNet h...