Intelligent multi-spectral IR image segmentation

Lu, Tingting; Luong, Andrew; Heim, Stephen E.; Patel, Maharshi; Chen, Kang; Chao, Tien‐Hsin; Chow, Edward; Torres, Gilbert

doi:10.1117/12.2262730

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there are many kinds of brake disc materials, the manual detection steps of the fastening clips on the plastic assembly of the automobile are cumbersome, and the colors are different. It leads to the phenomenon of gradient disappearance during the training process, which makes the algorithm fall into the local minimum value and cannot be seen [17][18][19]. erefore, at this stage, deep learning is difficult to solve the problem of complex background removal of auto parts such as brake discs and batteries [20].…”

Section: Related Workmentioning

confidence: 99%

Design of an Image Integrated Processing System for Improving Efficiency of Electric Vehicle Supporting Products

Wang

Lin

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

This study analyzes a solution that requires efficient and comprehensive processing of images of a large number of vehicles and their related parts, such as batteries, plastic fastening components, and brake discs, during the design investigation of electric vehicle accessories. The problem involves the extraction of the outer contours of different components, which is important to build a comprehensive image processing system that can handle different vehicle accessories. In this study, a comprehensive image processing system is proposed, which introduces an improved GrabCut and computer vision methods. It can complete the positioning of vehicle batteries, the fastening of automobile components, and the identification of brake discs, which improves the efficiency of inspection and design work. The improved GrabCut uses adaptive median filtering on the electric car accessory to reduce noise from the surface in variable degrees. The image is then sharpened using the Laplacian operator, followed by a contrast-limited histogram equalization (CLAHE) algorithm to boost the image brightness. We have compared our proposed work against existing techniques, i.e., the GrabCut algorithm, region growing algorithm, and K-means algorithm. The comparison clearly shows that our proposed work achieves a much better peak signal-to-noise ratio value as compared to the existing techniques.

show abstract