1. Multiple object characterization of recyclable domestic waste using binocular stereo vision.
- Author
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Wu, Qunbiao, Liang, Tao, Fang, Haifeng, Cao, Jin, Wang, Mingqiang, and He, Defang
- Subjects
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OBJECT recognition (Computer vision) , *BINOCULAR vision , *WASTE recycling , *STEREOSCOPIC cameras , *CAMERA calibration - Abstract
Recycling and recovery of domestic waste pose significant challenges in terms of accurate object positioning and ranging, areas where conventional methods, heavily reliant on monocular image processing, often fail due to limited distance data and reduced precision. This study introduces an innovative strategy for the localization and ranging of recyclable waste objects, leveraging the NanoDet-Plus model within a binocular stereo vision context. A dataset, the Binocular Recyclable Domestic Waste Dataset (BRDWD), was constructed, with transfer learning executed via the pretrained MULTI-TRASH monocular dataset. Procedures encompassed stereo camera calibration and rectification to address geometric distortions, while the integration of the Semi-Global Block Matching (SGBM) algorithm supplied disparity details for triangulation-based distance computations. Enhancements to the NanoDet-Plus model led to improved target accuracy, evidenced by a mean average precision at a threshold of 0.5 (mAP@0.5) reaching 91.88% and a detection speed of 15 frames per second (FPS). Assessments verified a ranging error below 5% within the 0.5 to 1.2-meter span, aligning well with the requirements of typical recyclable waste sorting and recycling situations. Additionally, a human-machine interface was developed utilizing Pyside6, enabling image uploads, real-time result visualization, and interactive process control, thereby furnishing pivotal advancements for the automation and intellectualization of waste categorization and recycling. [ABSTRACT FROM AUTHOR]
- Published
- 2025
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