Abstract
In cities, a large amount of municipal solid waste has impacted on the ecological environment significantly. Automatic and robust waste detection and classification is a promising and challenging problem in urban solid waste disposal. The performance of the classical detection and classification method is degraded by some factors, such as various occlusion and scale differences. To enhance the detection model robustness to occlusion and small items, we proposed a robust waste detection method based on a cascade adversarial spatial dropout detection network(Cascade ASDDN). The hard examples with occlusion in pyramid feature space are generated and used to adversarial training a detection network. Hard samples are generated by the spatial dropout module with Gradient-weighted Class Activation Mapping. The experiment verifies the effectiveness of our method on the 2020 Haihua AI challenge waste classification.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.