PPCNN: Object Detection using Fine-grained Feature Extraction and Localization

2021 ◽  
Vol 19 (2) ◽  
pp. 29-37
Author(s):  
Md Foysal Haque ◽  
Dae-Seong Kang
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Fine Grained

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

scholarly journals Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 45
Author(s):  
Marcin Woźniak
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
Computational Intelligence ◽  
Smart Sensor

The recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice [...]

DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection

2021 ◽  
Author(s):  
Yunqian He ◽  
Guihua Xia ◽  
Yongkang Luo ◽  
Li Su ◽  
Zhi Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Object Detection ◽  
3D Object ◽  
3D Object Detection

Efficient Fine-Grained Object Detection for Robot-Assisted WEEE Disassembly

2020 ◽  
pp. 17-22
Author(s):  
Ioannis Athanasiadis ◽  
Athanasios Psaltis ◽  
Apostolos Axenopoulos ◽  
Petros Daras
Keyword(s):  
Object Detection ◽  
Robot Assisted ◽  
Fine Grained

Oriented Object Detection with Fine-Grained Enhancement and Angle Constraint

2021 ◽  
Author(s):  
Mengyuan Wang ◽  
Xuanyu Zhang ◽  
Chuanbo Yu ◽  
Tingyi Guo ◽  
Jingxiao Gu ◽  
...  
Keyword(s):  
Object Detection ◽  
Fine Grained ◽  
Oriented Object
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