Deep learning for urban waste detection and classification: a survey of advances and challenges

Traditional waste management systems often fails to ensure safety and may cause health hazards for the workers. Depending on manual sorting and fixed infrastructure, systems increasingly fall short of sustainability goals. Recently, deep learning (DL) has revolutionized trash management, from garbage categorization and sorting to intelligent bin monitoring, and environmental effect assessment. This survey comprehensively reviews state-of-the-art deep learning techniques applied across the waste management lifecycle. Existing surveys mostly analyze different sorts of waste classification and lacks many practical challenges of applying DL methods to waste management. To address this research gap, we adopt a DL perspective to analyze automated waste management and segregation works and present research challenges from the implementation perspectives. Internet of Things (IoT) plays a very important role in such works. The problems of existing benchmark datasets are also discussed. The survey reports experimental results on representative benchmark datasets that are publicly available to signify the type of experimentation that could be conducted. Recent advances and hence, possible future research directions in the context of DL-based automated waste segregation have also been articulated.