A Novel Detection Method of Paper Defects Based on Visual Attention Mechanism

2011 ◽  
Vol 3 (3) ◽  
pp. 24-31 ◽  
Author(s):  
Ping Jiang ◽  
Tao Gao
Keyword(s):  
Visual Attention ◽  
Detection Method ◽  
Saliency Map ◽  
Attention Mechanism ◽  
Feature Maps ◽  
Seed Point ◽  
Multi Scale ◽  
Comparative Maps ◽  
Salient Points ◽  
Visual Attention Mechanism

In this paper, an improved paper defects detection method based on visual attention mechanism computation model is presented. First, multi-scale feature maps are extracted by linear filtering. Second, the comparative maps are obtained by carrying out center-surround difference operator. Third, the saliency map is obtained by combining conspicuity maps, which is gained by combining the multi-scale comparative maps. Last, the seed point of watershed segmentation is determined by competition among salient points in the saliency map and the defect regions are segmented from the background. Experimental results show the efficiency of the approach for paper defects detection.

A Novel Detection Method of Paper Defects Based on Visual Attention Mechanism

2013 ◽  
pp. 159-165 ◽  
Author(s):  
Ping Jiang ◽  
Tao Gao
Keyword(s):  
Visual Attention ◽  
Detection Method ◽  
Saliency Map ◽  
Attention Mechanism ◽  
Feature Maps ◽  
Seed Point ◽  
Multi Scale ◽  
Comparative Maps ◽  
Salient Points ◽  
Visual Attention Mechanism

In this paper, an improved paper defects detection method based on visual attention mechanism computation model is presented. First, multi-scale feature maps are extracted by linear filtering. Second, the comparative maps are obtained by carrying out center-surround difference operator. Third, the saliency map is obtained by combining conspicuity maps, which is gained by combining the multi-scale comparative maps. Last, the seed point of watershed segmentation is determined by competition among salient points in the saliency map and the defect regions are segmented from the background. Experimental results show the efficiency of the approach for paper defects detection.

The Application of Visual Attention Mechanism in Road Disaster Identification and early Warning System

2013 ◽  
Vol 385-386 ◽  
pp. 523-526
Author(s):  
Shu Yue Hua ◽  
Nan Feng Xiao
Keyword(s):  
Visual Attention ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Saliency Map ◽  
Attention Mechanism ◽  
Total System ◽  
Time Performance ◽  
Disaster Monitoring ◽  
Visual Attention Mechanism

Visual attention mechanism is introduced into the traditional road disaster monitoring and early warning system. In this system, the disaster region is the focus of attention (FOA), which happens to be the object needed to process. Ittis algorithm [1]was used to extract the saliency map, then quickly located the regions which may contain disaster according to saliency. The recognition and early warning of disaster can be completed, quickly. This method was tested snowstorms and rolling stones are simulated, and gave the corresponding experimental results. Experiment results show the correctness and efficiency of introducing visual attention mechanism into road disaster monitor and early warning system. It is of great significance and practical value for reducing the computation and improving real-time performance of the total system.

scholarly journals A Method of Waypoint Selection in Aerial Images for Vision Navigation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Lin Song ◽  
Yong-mei Cheng ◽  
Lu Yu ◽  
Liang Yu
Keyword(s):  
Visual Attention ◽  
Saliency Map ◽  
Attention Mechanism ◽  
Aerial Images ◽  
Low Rank ◽  
Support Vector ◽  
Suitability Analysis ◽  
Initial Image ◽  
Edge Based ◽  
Visual Attention Mechanism

We present a novel method to select waypoints from aerial images of candidate flying regions via matching suitability analysis, which is based on visual attention mechanism and feature attribute classification. At first, visual attention mechanism is used to get the saliency map of the initial image by low-rank recovery and sparse coding. The salient regions are selected to be as preparatory results with threshold constraint and nonmaxima suppression. Then we use support vector machine (SVM) to divide the preparatory results into two classes for suitable or unsuitable waypoints based on their feature attributes, which can be represented by two edge-based descriptors and two correlation-based descriptors. The experimental results show that the proposed method is valid and effective.

scholarly journals A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5

2021 ◽  
Vol 13 (9) ◽  
pp. 1619
Author(s):  
Bin Yan ◽  
Pan Fan ◽  
Xiaoyan Lei ◽  
Zhijie Liu ◽  
Fuzeng Yang
Keyword(s):  
Real Time ◽  
Apple Tree ◽  
Detection Method ◽  
Recognition Algorithm ◽  
Medium Size ◽  
Feature Maps ◽  
Detection Algorithms ◽  
Fusion Mode ◽  
Visual Attention Mechanism ◽  
Tree Image

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

scholarly journals U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation

2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Chenjie Wang ◽  
Chengyuan Li ◽  
Jun Liu ◽  
Bin Luo ◽  
Xin Su ◽  
...  
Keyword(s):  
Moving Objects ◽  
Object Segmentation ◽  
Contextual Information ◽  
Attention Mechanism ◽  
Moving Object ◽  
Feature Maps ◽  
Moving Object Segmentation ◽  
Practical Applications ◽  
Multi Scale ◽  
Spatial Redundancy

Most scenes in practical applications are dynamic scenes containing moving objects, so accurately segmenting moving objects is crucial for many computer vision applications. In order to efficiently segment all the moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested octave U-structure network with a multi-scale attention mechanism, called U2-ONet. U2-ONet takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs. Each stage of U2-ONet is filled with the newly designed octave residual U-block (ORSU block) to enhance the ability to obtain more contextual information at different scales while reducing the spatial redundancy of the feature maps. In order to efficiently train the multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding knowledge-matching loss to keep the optimization consistent. The experimental results show that the proposed U2-ONet method can achieve a state-of-the-art performance in several general moving object segmentation datasets.

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