A robust framework combined saliency detection and image recognition for garbage classification

• Published in: Waste management (Elmsford) Vol. 140; pp. 193 - 203
• Main Authors: Qin, Jiongming, Wang, Cong, Ran, Xu, Yang, Shaohua, Chen, Bin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2022
• Subjects: Algorithms; computers; data collection; Data fusion; Garbage; Garbage classification; image analysis; Image Processing, Computer-Assisted; Image segmentation; municipal solid waste; Saliency detection; waste management; Garbage classification; Image segmentation; Data fusion; Saliency detection
• ISSN: 0956-053X; 1879-2456; 1879-2456
• DOI: 10.1016/j.wasman.2021.11.027

•A robust framework combined saliency detection and image classification is proposed.•The smallest rectangle containing the saliency region is used for segmentation.•Data fusion is used to generate synthetic images for improving the robustness. Using deep learning to solve garbage classification has become a hot topic in computer version. The most widely used garbage dataset Trashnet only has garbage images with a white board as background. Previous studies based on Trashnet focus on using different networks to achieve a higher classification accuracy without considering the complex backgrounds which might encounter in practical applications. To solve this problem, we propose a framework that combines saliency detection and image classification to improve the generalization performance and robustness. A saliency network Salinet is adopted to obtain the garbage target area. Then, a smallest rectangle containing this area is created and used to segment the garbage. A classification network Inception V3 is used to identify the segmented garbage image. Images of the original Trashnet are fused with complex backgrounds of the other saliency detection datasets. The fused and original Trashnet are used together for training to improve the robustness to noises and complex backgrounds. Compared with the image classification networks and classic target detection algorithms, the proposed framework improves the accuracy of 0.50% − 15.79% on the testing sets fused with complex backgrounds. In addition, the proposed framework achieves the best performance with a gain of 4.80% in accuracy on the collected actual dataset. The comparisons prove that our framework is more robust to garbage classification in complex backgrounds. This method can be applied to smart trash cans to achieve automatic garbage classification.