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MSI-RGB Dual-Source Multiscale Fusion-Based Solid Waste Object Detection

delete2024-01-01
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PRE
AI
H
Haonan Liu
J
Jianhong Yang *
H
Huaiying Fang
T
Tianchen Ji
Z
Zhenxing Cai
DOI:10.1109/TIM.2024.3480227delete
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摘要

摘要

En 中文
This article focuses on the significant challenges faced by single-modal visual sensors in object detection under the fine-sorting process of solid waste. Object detection using a single sensor has inherent limitations due to the lack of comprehensive material and contour information about the object. Therefore, this article proposes a real-time detection network based on the MSI-RGB dual-source multiscale fusion network (DMFNet) and introduces a coarse-to-fine dual-stage band selection (dual-stage BS) method for reducing spectral redundant information. As a case study, we focus on plastic solid waste and build a dual-sensor experimental platform to collect color and hyperspectral images (HSIs) using a face-matrix color camera and a hyperspectral camera. Specifically, the dual-stage BS method is employed to analyze the hyper-spectral data and a small number of spectral bands were selected to generate a multispectral image (MSI) as the first input source for DMFNet. Meanwhile, the RGB image serves as the second input source for DMFNet. First, a spectral image feature extraction backbone (SIFEBackbone) and a multiscale stacking unit (MSU) are designed to extract multiscale features from spectral images fully. Second, a multibranch fusion unit (MBFU) is designed to realize the fusion of multiscale features with the spectral feature maps in the RGB-enhanced feature extraction stage. The experimental results show that dual-stage BS exhibits advantages over other methods, while DMF-YOLOv7, which integrates DMFNet and YOLOv7 detectors, improves the average precision and recall by 7.2% and 5.5%, respectively, compared with the original YOLOv7, and the mAP reaches 95.4% at IOU =0.5. It effectively reduces the material misrecognition problem and reflects the advantages of the dual-sensor algorithm. The source code is available at: https://github.com/Caicai-D/DMFNet.
Keyword:
Feature extraction
Waste materials
Hyperspectral imaging
Solid modeling
Plastics
Deep learning
Cameras
Analytical models
Data models
Accuracy
Dual-source fusion technology
hyperspectral imagery
multispectral feature extraction
object detection
spectral band selection

期刊

IEEE Transactions on Instrumentation and Measurement 封面图
IEEE Transactions on Instrumentation and Measurement
IF:
5.9
论文数:
2.0W
被引数:
5.8W

机构

F
fuzhou university
学者数:
3.3W
论文数: 2.1W
被引数: 31
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