We extensively compare, qualitatively and quantitatively, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted just two years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for state-of-the-art models, provide useful hints towards constructing more challenging large scale datasets and better saliency models. Finally, we propose probable solutions for tackling several open problems such as evaluation scores and dataset bias, which also suggest future research directions in the rapidly-growing field of salient object detection.

Salient Object Detection: A Benchmark

We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven challenging data sets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted three years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for the state-of-the-art models, provide useful hints toward constructing more challenging large-scale data sets and better saliency models. Finally, we propose probable solutions for tackling several open problems, such as evaluation scores and data set bias, which also suggest future research directions in the rapidly growing field of salient object detection.

/pdf/salient-object-detection-a-benchmark-3cltvczcnw.pdf

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.

/pdf/salient-object-detection-a-survey-1pehc7ghwi.pdf

Salient Object Detection: A Survey

Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

/pdf/state-of-the-art-in-defect-detection-based-on-machine-vision-2rtzvgsnqy.pdf

State of the Art in Defect Detection Based on Machine Vision

Bearing defect inspection based on machine vision

This paper describes a novel hierarchy algorithm for extracting bridges over water in optical images. To reduce the omission of bridges by searching the edge, we extract the river regions which the bridges are included in. Firstly, we segment the optical image to get the coarse water bodies using iterative threshold, eliminate the noise regions and add the missing regions based on k-means clustering with texture information and spatial coherence. Then, the blanks are connected based on shape features and candidate bridge regions are segmented from river regions. Finally, the bridges are verified by geometric information and the ubiety between bridges and river. The results show that this approach is efficient and effective for extracting bridges in satellite image from Google Earth and in aerial optical images acquired by unmanned aerial vehicle.

Automatic Bridge Extraction for Optical Images

We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves four steps. Firstly, both the motion history image and the tracking trajectory are employed to extract candidate target regions. Secondly, the spatial-temporal saliency is used to detect moving objects in the candidate regions. Thirdly, the previous detected objects are tracked by mean shift in the current frame. And finally, the detection results are fused with the tracking results to get refined detection results, in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.

Tracking-based moving object detection

We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves three steps. Firstly, the spatial-temporal saliency is employed to detect moving objects. Secondly, the detected objects are tracked by mean shift in the subsequent frames. Finally, the saliency results are fused with the weight map generated by tracking to get refined detection results, and in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.

This paper focuses on image registration between sequential frames in aerial infrared video. To address this problem, we propose an efficient approach based on an improved Smallest Univalue Segment Assimilating Nucleus (SUSAN) corner detector and a novel corner rejection strategy. Firstly, the SUSAN corners are detected via the concatenation of enlarging the radius of original detector and down-sampling. Then, the corner rejection strategy is applied to remove the corners on moving objects. After that, Histograms of Oriented Gradients (HOG) is used to form the descriptor of each corner, followed by a feature matching phase to obtain correspondences of corners. Finally, RANdom Sample Consensus (RANSAC) is performed to estimate the parameters of the transformation model with the correspondences. Experimental results show that our approach is fast and stable for image registration in aerial infrared videos.

A Novel Image Registration Algorithm in Aerial Infrared Videos

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Papers

Bearing defect inspection based on machine vision

Automatic Bridge Extraction for Optical Images

Tracking-based moving object detection

Tracking-based moving object detection

A Novel Image Registration Algorithm in Aerial Infrared Videos