Waste Classification Using Improved CNN Architecture

Chhabra, Megha; Sharan, Bhagwati; Gupta, Keshav; Astya, Rani

doi:10.2139/ssrn.4157549

Cited by 3 publications

(1 citation statement)

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“…YOLO approaches detection by segmenting frames into grids, striving to pinpoint objects within each. Utilizing a Non-max suppression filter, only the most prominent bounding boxes within a grid are retained, ensuring optimal outcomes [25]. Subsequent to the original YOLO's inception, several iterations were developed to augment its efficacy, notably YOLO v2 and YOLO v7.…”

Section: Related Workmentioning

confidence: 99%

Efficient Differentiation of Biodegradable and Non-Biodegradable Municipal Waste Using a Novel MobileYOLO Algorithm

Suman,

Arulanantham

2023

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In the realm of waste management, the accurate identification of biodegradable and nonbiodegradable items remains a critical challenge. An advanced real-time object detection method, termed "MobileYOLO", was proposed, leveraging the strengths of the YOLO v4 framework. The MobileNetv2 network was integrated, and a section of the conventional computation was substituted with depth-wise separable convolutions utilizing the PAnet and head network. To enhance feature expressiveness capabilities during feature fusion, a refined lightweight channel attention mechanism, known as Efficient Channel Attention (ECA), was introduced. The Improved Single Stage Headless (ISSH) context module was incorporated into the micro-object identification branch to broaden the receptive field. Evaluations conducted on the KITTI dataset indicated an impressive accuracy of 95.7%. Remarkably, when compared to the standard YOLOv4, the MobileYOLO model exhibited a reduction in model parameters by 53.12M, a decrease in connectivity size by one-fifth, and an augmentation in detection speed by 85%.

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Section: Related Workmentioning

confidence: 99%