Poster + Presentation + Paper
10 October 2020 A robust waste detection method based on cascade adversarial spatial dropout detection network
Author Affiliations +
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
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Computer vision technology

Sensors

Solids

Visual process modeling

RELATED CONTENT


Back to Top