Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection
Industrial visual detection is an essential part in modern industry for equipment maintenance and inspection. With the recent progress of deep learning, advanced industrial object detectors are built for smart industrial applications. However, deep learning methods are known data-hungry: the processes of data collection and annotation are labor-intensive and time-consuming. It is especially impractical in industrial scenarios to collect publicly available datasets due to the inherent diversity and privacy. In this paper, we explore automation of industrial visual inspection and propose a segmentation-aggregation framework to learn object detectors from weakly annotated visual data. The used minimum annotation is only image-level category labels without bounding boxes. The method is implemented and evaluated on collected insulator images and public PASCAL VOC benchmarks to verify its effectiveness. The experiments show that our models achieve high detection accuracy and can be applied in industry to achieve automatic visual inspection with minimum annotation cost.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningDiversityobject-detectionObject DetectionWeakly-supervised LearningWeakly Supervised Object DetectionSimilar Papers 제목 키워드 기반
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects
Automatic visual inspection using machine learning-based methods plays a key role in achieving zero-defect policies in industry. Research on anomaly detection approaches is constrained by the availability of datasets tha…
Anomaly DetectionDefect DetectionSelf-Supervised Anomaly DetectionSupervised Anomaly Detection+3SIAD: Self-supervised Image Anomaly Detection System
Recent trends in AIGC effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-term support to the online application. T…
Anomaly DetectionCloud ComputingSelf-Supervised LearningAttention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study
Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high…
Anomaly DetectionMixed supervision for surface-defect detection: from weakly to fully supervised learning
Deep-learning methods have recently started being employed for addressing surface-defect detection problems in industrial quality control. However, with a large amount of data needed for learning, often requiring high-pr…
Anomaly DetectionDefect DetectionSupervised Defect DetectionWeakly Supervised Defect DetectionRethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks
Vision foundation models pretrained on web-scale data have recently shown strong transfer capabilities on many downstream tasks, but their effectiveness for industrial visual inspection remains unclear. Industrial data d…
Semantic SegmentationInstance SegmentationTransfer LearningObject Detection