A robust waste detection method based on cascade adversarial spatial dropout detection network

Bo Feng; Kun Ren; Qingyang Tao; Xuejin Gao

doi:10.1117/12.2575394

10 October 2020 A robust waste detection method based on cascade adversarial spatial dropout detection network

Bo Feng, Kun Ren, Qingyang Tao, Xuejin Gao

Proceedings Volume 11550, Optoelectronic Imaging and Multimedia Technology VII; 115500Q (2020) https://doi.org/10.1117/12.2575394
Event: SPIE/COS Photonics Asia, 2020, Online Only

Conference Poster

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.

Conference Presentation

Citation Download Citation

Bo Feng, Kun Ren, Qingyang Tao, and Xuejin Gao "A robust waste detection method based on cascade adversarial spatial dropout detection network", Proc. SPIE 11550, Optoelectronic Imaging and Multimedia Technology VII, 115500Q (10 October 2020); https://doi.org/10.1117/12.2575394

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