MINet: Multi-scale Interactive Network for Real-time Salient Object Detection of Strip Steel Surface Defects
The automated surface defect detection is a fundamental task in industrial production, and the existing saliencybased works overcome the challenging scenes and give promising detection results. However, the cutting-edge efforts often suffer from large parameter size, heavy computational cost, and slow inference speed, which heavily limits the practical applications. To this end, we devise a multi-scale interactive (MI) module, which employs depthwise convolution (DWConv) and pointwise convolution (PWConv) to independently extract and interactively fuse features of different scales, respectively. Particularly, the MI module can provide satisfactory characterization for defect regions with fewer parameters. Embarking on this module, we propose a lightweight Multi-scale Interactive Network (MINet) to conduct real-time salient object detection of strip steel surface defects. Comprehensive experimental results on SD-Saliency-900 dataset, which contains three kinds of strip steel surface defect detection images (i.e., inclusion, patches, and scratches), demonstrate that the proposed MINet presents comparable detection accuracy with the state-of-the-art methods while running at a GPU speed of 721FPS and a CPU speed of 6.3FPS for 368*368 images with only 0.28M parameters. The code is available at https://github.com/Kunye-Shen/MINet.
Code (1)
Tasks
CPUDefect DetectionGPUobject-detectionObject DetectionSalient Object DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multi-scale Interactive Network for Salient Object Detection
Deep-learning based salient object detection methods achieve great progress. However, the variable scale and unknown category of salient objects are great challenges all the time. These are closely related to the utiliza…
DecoderObjectobject-detectionRGB Salient Object Detection+1DMInet: An Accurate and Highly Flexible Deep Learning Framework for Drug Membrane Interaction with Membrane Selectivity
Drug membrane interaction is a very significant bioprocess to consider in drug discovery. Here, we propose a novel deep learning framework coined DMInet to study drug-membrane interactions that leverages large-scale Mart…
Drug DiscoveryFAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flow
Semi-supervised video object segmentation (VOS) aims to segment a few moving objects in a video sequence, where these objects are specified by annotation of first frame. The optical flow has been considered in many exist…
Optical Flow EstimationSegmentationSemantic SegmentationSemi-Supervised Video Object Segmentation+2SimiNet: a Novel Method for Quantifying Brain Network Similarity
Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the properties of nodes and edges. Interestingly, m…
Graph SimilarityObject CategorizationExplicitly antisymmetrized neural network layers for variational Monte Carlo simulation
The combination of neural networks and quantum Monte Carlo methods has arisen as a path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers wi…
DiagnosticVariational Monte Carlo