Deeper and Mixed Supervision for Salient Object Detection in Automated Surface Inspection

In recent years, researches in the field of salient object detection have been widely made in many industrial visual inspection tasks. Automated surface inspection (ASI) can be regarded as one of the most challenging tasks in computer vision because of its high cost of data acquisition, serious imbalance of test samples, and high real-time requirement. Inspired by the requirements of industrial ASI and the methods of salient object detection (SOD), a task mode of defect type classification plus defect area segmentation and a novel deeper and mixed supervision network (DMS) architecture is proposed. The backbone network ResNeXt-101 was pretrained on ImageNet. Firstly, we extract five multiscale feature maps from backbone and concatenate them layer by layer. In addition, to obtain the classification prediction and saliency maps in one stage, the image-level and pixel-level ground truth is trained in a same side output network. Supervision signal is imposed on each side layer to realize deeper and mixed training for the network. Furthermore, the DMS network is equipped with residual refinement mechanism to refine the saliency maps of input images. We evaluate the DMS network on 4 open access ASI datasets and compare it with other 20 methods, which indicates that mixed supervision can significantly improve the accuracy of saliency segmentation. Experiment results show that the proposed method can achieve the state-of-the-art performance.

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Comparing Salient Object Detection Results without Ground Truth

Computer Vision – ECCV 2014 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-10578-9_6 ◽

2014 ◽

pp. 76-91 ◽

Cited By ~ 17

Author(s):

Long Mai ◽

Feng Liu

Keyword(s):

Object Detection ◽

Ground Truth ◽

Salient Object Detection ◽

Salient Object

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Multi-Scale Global Contrast CNN for Salient Object Detection

Sensors ◽

10.3390/s20092656 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2656

Author(s):

Weijia Feng ◽

Xiaohui Li ◽

Guangshuai Gao ◽

Xingyue Chen ◽

Qingjie Liu

Keyword(s):

Object Detection ◽

Salient Object Detection ◽

Salient Object ◽

Multi Scale ◽

Global Contrast ◽

Saliency Maps ◽

Visual Contrast ◽

Contrast Information ◽

Homogeneous Regions ◽

Global And Local

Salient object detection (SOD) is a fundamental task in computer vision, which attempts to mimic human visual systems that rapidly respond to visual stimuli and locate visually salient objects in various scenes. Perceptual studies have revealed that visual contrast is the most important factor in bottom-up visual attention process. Many of the proposed models predict saliency maps based on the computation of visual contrast between salient regions and backgrounds. In this paper, we design an end-to-end multi-scale global contrast convolutional neural network (CNN) that explicitly learns hierarchical contrast information among global and local features of an image to infer its salient object regions. In contrast to many previous CNN based saliency methods that apply super-pixel segmentation to obtain homogeneous regions and then extract their CNN features before producing saliency maps region-wise, our network is pre-processing free without any additional stages, yet it predicts accurate pixel-wise saliency maps. Extensive experiments demonstrate that the proposed network generates high quality saliency maps that are comparable or even superior to those of state-of-the-art salient object detection architectures.

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Dual-Branch Feature Fusion Network for Salient Object Detection

Photonics ◽

10.3390/photonics9010044 ◽

2022 ◽

Vol 9 (1) ◽

pp. 44

Author(s):

Zhehan Song ◽

Zhihai Xu ◽

Jing Wang ◽

Huajun Feng ◽

Qi Li

Keyword(s):

Object Detection ◽

Feature Fusion ◽

Local Information ◽

Salient Object Detection ◽

Large Field ◽

Wide Field ◽

Global Information ◽

Layer By Layer ◽

Salient Object ◽

Information Layer

Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view.

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Progressive Feature Polishing Network for Salient Object Detection

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6892 ◽

2020 ◽

Vol 34 (07) ◽

pp. 12128-12135 ◽

Cited By ~ 1

Author(s):

Bo Wang ◽

Quan Chen ◽

Min Zhou ◽

Zhiqiang Zhang ◽

Xiaogang Jin ◽

...

Keyword(s):

Object Detection ◽

State Of The Art ◽

Hierarchical Structures ◽

Salient Object Detection ◽

Salient Object ◽

Post Processing ◽

Feature Maps ◽

Multiple Feature ◽

Benchmark Datasets ◽

Multi Level

Feature matters for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present Progressive Feature Polishing Network (PFPN), a simple yet effective framework to progressively polish the multi-level features to be more accurate and representative. By employing multiple Feature Polishing Modules (FPMs) in a recurrent manner, our approach is able to detect salient objects with fine details without any post-processing. A FPM parallelly updates the features of each level by directly incorporating all higher level context information. Moreover, it can keep the dimensions and hierarchical structures of the feature maps, which makes it flexible to be integrated with any CNN-based models. Empirical experiments show that our results are monotonically getting better with increasing number of FPMs. Without bells and whistles, PFPN outperforms the state-of-the-art methods significantly on five benchmark datasets under various evaluation metrics. Our code is available at: https://github.com/chenquan-cq/PFPN.

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Fast Salient Object Detection in Non-stationary Video Sequences Based on Spatial Saliency Maps

Smart Innovation, Systems and Technologies - Intelligent Interactive Multimedia Systems and Services 2016 ◽

10.1007/978-3-319-39345-2_11 ◽

2016 ◽

pp. 121-132 ◽

Cited By ~ 4

Author(s):

Margarita Favorskaya ◽

Vladimir Buryachenko

Keyword(s):

Object Detection ◽

Salient Object Detection ◽

Video Sequences ◽

Salient Object ◽

Saliency Maps

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Exploring Image Enhancement for Salient Object Detection in Low Light Images

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3414839 ◽

2021 ◽

Vol 17 (1s) ◽

pp. 1-19

Author(s):

Xin Xu ◽

Shiqin Wang ◽

Zheng Wang ◽

Xiaolong Zhang ◽

Ruimin Hu

Keyword(s):

Object Detection ◽

Image Enhancement ◽

Ground Truth ◽

Salient Object Detection ◽

Salient Object ◽

Low Light ◽

Low Contrast ◽

Local Content ◽

The Difference ◽

Public Datasets

Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights. This degradation results in severe object information loss in the degraded image modality, which makes the salient object detection more challenging due to low contrast property and artificial light influence. However, existing salient object detection models are developed based on the assumption that the images are captured under a sufficient brightness environment, which is impractical in real-world scenarios. In this work, we propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model directly embeds the physical lighting model into the deep neural network to describe the degradation of low light images, in which the environment light is treated as a point-wise variate and changes with local content. Moreover, a Non-Local-Block Layer is utilized to capture the difference of local content of an object against its local neighborhood favoring regions. To quantitative evaluation, we construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results on four public datasets and our benchmark dataset.

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Region Diversity Based Saliency Density Maximization for Salient Object Detection

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e96.a.394 ◽

2013 ◽

Vol E96.A (1) ◽

pp. 394-397 ◽

Cited By ~ 1

Author(s):

Xin HE ◽

Huiyun JING ◽

Qi HAN ◽

Xiamu NIU

Keyword(s):

Object Detection ◽

Salient Object Detection ◽

Salient Object

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Saliency detection in deep learning era: trends of development

Information and Control Systems ◽

10.31799/1684-8853-2019-3-10-36 ◽

2019 ◽

pp. 10-36 ◽

Cited By ~ 2

Author(s):

M. N. Favorskaya ◽

L. C. Jain

Keyword(s):

Deep Learning ◽

Object Detection ◽

Event Detection ◽

Visual Analysis ◽

Saliency Detection ◽

Salient Object Detection ◽

Public Image ◽

Detection Methods ◽

Salient Object ◽

Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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