Detection Approach Based on an Improved Faster RCNN for Brace Sleeve Screws in High-Speed Railways

Liu, Zhigang; Lyu, Yang; Wang, Liyou; Han, Zhiwei

doi:10.1109/tim.2019.2941292

Cited by 52 publications

(24 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ese methods mainly employ object features extracted manually for establishing the parameters of the algorithm. However, the rapid advancement of deep learning in recent years has led to the development of numerous object detection methods based on this advanced technology [11][12][13][14][15][16][17][18][19][20][21][22].…”

Section: Related Workmentioning

confidence: 99%

“…It uses the region proposal network (RPN), which solves the inefficient selection problem of proposal regions in target detection tasks. In addition, some distinguished researchers [14,15] proposed improved algorithms for the faster R-CNN. For example, feature pyramid networks (FPN) [14], compared to regular feature pyramids, proposed a feature pyramid structure which enables independent prediction in each level of pyramid.…”

Section: R-cnn-based Object Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

Chen

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The accuracy of object detection based on kitchen appliance scene images can suffer severely from external disturbances such as various levels of specular reflection, uneven lighting, and spurious lighting, as well as internal scene-related disturbances such as invalid edges and pattern information unrelated to the object of interest. The present study addresses these unique challenges by proposing an object detection method based on improved faster R-CNN algorithm. The improved method can identify object regions scattered in various areas of complex appliance scenes quickly and automatically. In this paper, we put forward a feature enhancement framework, named deeper region proposal network (D-RPN). In D-RPN, a feature enhancement module is designed to more effectively extract feature information of an object on kitchen appliance scene. Then, we reconstruct a U-shaped network structure using a series of feature enhancement modules. We have evaluated the proposed D-RPN on the dataset we created. It includes all kinds of kitchen appliance control panels captured in nature scene by image collector. In our experiments, the best-performing object detection method obtained a mean average precision mAP value of 89.84% in the testing dataset. The test results show that the proposed improved algorithm achieves higher detecting accuracy than state-of-the-art object detection methods. Finally, our proposed detection method can further be used in text recognition.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: R-cnn-based Object Detectionmentioning

confidence: 99%

Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

Chen

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…e RPN is responsible for extracting candidate regions [22,23], whose architecture is illustrated in Figure 2. It receives the convolutional feature maps from the basic feature extraction network and convolves each 3 * 3 sliding window into a 256-dimensional feature vector via convolution kernels.…”

Section: Feature Extraction Of the Datasetsmentioning

confidence: 99%

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

Feng

Tao

Lee

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

show abstract

“…In recent years, machine learning-based algorithms, including statistical machine learning [ 28 , 29 , 30 ] and deep neural networks [ 31 , 32 , 33 , 34 , 35 , 36 , 37 ], have been widely applied in the suspicious object detection for the MMW image. The literature [ 38 ] proposed a method that combines image processing with statistical machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

CNN with Pose Segmentation for Suspicious Object Detection in MMW Security Images

Meng

Zhang

Wang

2020

Sensors

View full text Add to dashboard Cite

Millimeter-wave (MMW) imaging scanners can see through clothing to form a three-dimensional holographic image of the human body and suspicious objects, providing a harmless alternative for non-contacting searches in security check. Suspicious object detection in MMW images is challenging, since most of them are small, reflection-weak, shape, and reflection-diverse. Conventional detectors with artificial neural networks, like convolution neural network (CNN), usually take the problem of finding suspicious objects as an object recognition task, yielding difficulties in developing large-amount and complete sample sets of objects. In this paper, a new algorithm is developed using the human pose segmentation followed by the deep CNN detection. The algorithm is emphasized to learn the similarity with humans’ body clutter applied to training corresponding CNNs after the image segmentation base of the pose estimation. Moreover, the suspicious object recognition in the MMW image is converted to a binary classification task. Instead of recognizing all sorts of suspicious objects, the CNN detector determines whether the body part images present the abnormal patterns containing suspicious objects. The proposed algorithm that is based on CNN with the pose segmentation has concise configuration, but optimal performance in the suspicious object detection. Extensive experiments confirm the effectiveness and superiority of the proposal.

show abstract

Detection Approach Based on an Improved Faster RCNN for Brace Sleeve Screws in High-Speed Railways

Cited by 52 publications

References 13 publications

Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

CNN with Pose Segmentation for Suspicious Object Detection in MMW Security Images

Contact Info

Product

Resources

About