scholarly journals A time-saving automobile assembly state monitoring system based on channel-pruned YOLOv4 algorithm

2021 ◽  
Author(s):  
Jiang Daqi ◽  
Hong Wang
Keyword(s):  
Neural Network ◽  
Monitoring System ◽  
Target Detection ◽  
Detection Algorithm ◽  
Experimental Result ◽  
State Monitoring ◽  
Time Saving ◽  
Industrial Environment ◽  
Automobile Assembly ◽  
Speed And Accuracy

Abstract A time-saving automobile assembly state monitoring system in industrial environment is presented in this paper. The system only needs to input a video which contains the whole detected parts and manually label in the first frame. By finding the best point for tracking and tracking the point, the dataset can be automatically generated which saves time spent on manufacturing the dataset and makes the assembly state monitoring system easy to deploy into a practical industrial environment. The target detection algorithm uses the channel-pruned YOLOv4 neural network. The experimental result shows the algorithm balances speed and accuracy. Compared to original YOLOv4, our proposed method is two times faster and the mAP is nearly equal to it. It shows that the channel pruning process dynamically improves the speed of the forward propagation without sacrifice accuracy.

scholarly journals Image target detection algorithm compression and pruning based on neural network

2021 ◽  
Vol 18 (2) ◽  
pp. 499-516
Author(s):  
Yan Sun ◽  
Zheping Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Network Model ◽  
Detection Algorithm ◽  
Image Feature ◽  
Propagation Mode ◽  
Learning Technology ◽  
Image Detection ◽  
Detection Algorithms

The main purpose of target detection is to identify and locate targets from still images or video sequences. It is one of the key tasks in the field of computer vision. With the continuous breakthrough of deep machine learning technology, especially the convolutional neural network model shows strong Ability to extract image feature in the field of digital image processing. Although the model research of target detection based on convolutional neural network is developing rapidly, but there are still some problems in practical applications. For example, a large number of parameters requires high storage and computational costs in detected model. Therefore, this paper optimizes and compresses some algorithms by using early image detection algorithms and image detection algorithms based on convolutional neural networks. After training and learning, there will appear forward propagation mode in the application of CNN network model, providing the model for image feature extraction, integration processing and feature mapping. The use of back propagation makes the CNN network model have the ability to optimize learning and compressed algorithm. Then research discuss the Faster-RCNN algorithm and the YOLO algorithm. Aiming at the problem of the candidate frame is not significant which extracted in the Faster- RCNN algorithm, a target detection model based on the Significant area recommendation network is proposed. The weight of the feature map is calculated by the model, which enhances the saliency of the feature and reduces the background interference. Experiments show that the image detection algorithm based on compressed neural network image has certain feasibility.

Target Detection of Sonar Image Based on Bis-Parameter with Adaptive Windows

2013 ◽  
Vol 347-350 ◽  
pp. 3407-3410
Author(s):  
Ke Li ◽  
Zhong Liu ◽  
Sheng Liang Hu ◽  
Yang Liu
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
High Accuracy ◽  
New Method ◽  
Experimental Result ◽  
Image Process ◽  
Sar Image ◽  
Sonar Image

The detection algorithm CFAR is very mature in SAR image process field and the efficiency is very good. In the paper, CFAR is tried to be used in sonar image process. In order to solve the problem that the target part leaking to the background, a new method target detection of sonar image based on bis-parameter with adaptive windows is proposed. The size of adaptive windows can be adjusted to totally cover different targets. The experimental result showed that the complex multi target can be detected by the proposed method in a high accuracy.

scholarly journals Classification of Radar Environment Using Ensemble Neural Network with Variation of Hidden Neuron Number

2017 ◽  
Vol 17 (1) ◽  
pp. 19 ◽  
Author(s):  
Budiman Putra Asmaur Rohman ◽  
Dayat Kurniawan
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Ensemble Classifier ◽  
Radar System ◽  
Constant False Alarm Rate ◽  
Neuron Number ◽  
Radar Systems ◽  
Ensemble Neural Network

Target detection is a mandatory task of radar system so that the radar system performance is mainly determined by its detection rate. Constant False Alarm Rate (CFAR) is a detection algorithm commonly used in radar systems. This method is divided into several approaches which have different performance in the different environments. Therefore, this paper proposes an ensemble neural network based classifier with a variation of hidden neuron number for classifying the radar environments. The result of this research will support the improvement of the performance of the target detection on the radar systems by developing such an adaptive CFAR. Multi-layer perceptron network (MLPN) with a single hidden layer is employed as the structure of base classifiers. The first step of this research is the evaluation of the hidden neuron number giving the highest accuracy of classification and the simplicity of computation. According to the result of this step, the three best structures are selected to build an ensemble classifier. On the ensemble structure, all of those three MLPN outputs then be collected and voted for getting the majority result in order to decide the final classification. The three possible radar environments investigated are homogeneous, multiple-targets and clutter boundary. According to the simulation results, the ensemble MLPN provides a higher detection rate than the conventional single MLPNs. Moreover, in the multiple-target and clutter boundary environments, the proposed method is able to show its highest performance.

scholarly journals Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Meiping Jiang ◽  
Sanlin Lei ◽  
Junhui Zhang ◽  
Liqiong Hou ◽  
Meixiang Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Intelligence ◽  
Target Detection ◽  
Ultrasound Imaging ◽  
Breast Density ◽  
Multimodal Imaging ◽  
Detection Algorithm ◽  
Malignant Breast ◽  
Breast Lumps

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant ( P  < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance ( P  < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.

scholarly journals Ship Target Detection Algorithm Based on Improved Faster R-CNN

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 959 ◽  
Author(s):  
Qi ◽  
Li ◽  
Chen ◽  
Wang ◽  
Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Texture Features ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Marine Transportation ◽  
Computational Speed ◽  
Neural Network Algorithm ◽  
Detection Search

Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.

RGB-D Image Multi-Target Detection Method Based on 3D DSF R-CNN

2019 ◽  
Vol 33 (08) ◽  
pp. 1954026 ◽  
Author(s):  
Qi Hu ◽  
Lang Zhai
Keyword(s):  
Target Detection ◽  
Color Image ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Experimental Result ◽  
Depth Information ◽  
Two Dimensional ◽  
Information Need ◽  
Image Detection ◽  
Depth Cameras

At present, the application of deep learning algorithms in two-dimensional color image detection is being continuously innovated and broken. With the popularity of depth cameras, color image detection methods with depth information need to be upgraded. To solve this problem, a multi-target detection algorithm based on 3D DSF R-CNN (Double Stream Faster R-CNN, Convolution Neural Network based on Candidate Region) is proposed in this paper. The RGB information and the depth information of the image are given to two input elements of the convolution network with the same structure and weight sharing, and an optimal fusion weight algorithm is used to determine the weight of the fusion target in accordance with the recognition accuracy of the recognition targets under the single modal information, so as to ensure the most efficient fusion result. After several convolution operations, the independent features are extracted and the two networks are fused according to the optimal weights in the convolution layer. With the conducting of convolution and extract the fused features, and finally get the output through the full link layer. Compared with the previous two-dimensional convolution network algorithm, this algorithm improves the detection rate and success rate while ensuring the detection time. The experimental result shows that this method has strong robustness for complex illumination and partial occlusion, and has excellent detection results under non-restrictive conditions.

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