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16 August 2024 CR-FOD: foreign object detection based on contrastive analysis of key regions
Peigang Liu, Qingsong Lan, Peng He, Ping Deng, Cheng Liang, Yuming Wang
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Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 132301U (2024) https://doi.org/10.1117/12.3036480
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China
Abstract
In the realms of flight safety, agricultural monitoring and medical diagnostics, the detection of foreign objects within images processing technology is of importance. While recent deep learning-based detection methods have enhanced overall performance compared to traditional image techniques, they often encounter challenges in identifying small or novel foreign objects due to data biases. To overcome this limitation, a new approach termed foreign object detection based on contrastive analysis of key regions (CR-FOD) has been introduced. CR-FOD is bifurcated into two phases: Foreign Object Promoter (FOP) and Contrastive Region Object Detection (CROD). During FOP, potential foreign object locations are marked through key point comparison. In the CROD phase, Objects are extracted from the inference image and the corresponding background image based on key-point prompt information by FOP. Finally, the similarity of the objects is compared by CROD to determine the presence of a foreign object at the location. In addition, the efficacy of CR-FOD is confirmed through experimental testing, demonstrating its superior performance in detecting a wide array of foreign objects, particularly those that are minute or previously not encountered during the training datasets.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Peigang Liu, Qingsong Lan, Peng He, Ping Deng, Cheng Liang, and Yuming Wang "CR-FOD: foreign object detection based on contrastive analysis of key regions", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 132301U (16 August 2024); https://doi.org/10.1117/12.3036480
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KEYWORDS
Object detection
Education and training
Image segmentation
Deep learning
Image processing
Image fusion
Target detection
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