Study on the Method of Pedestrian Detection in Automobile Safety System

2012 ◽  
Vol 580 ◽  
pp. 118-121
Author(s):  
Zhong Hao Bai ◽  
Zhi Peng Ding ◽  
Qiang Yan
Keyword(s):  
Traffic Accidents ◽  
Detection Method ◽  
Detection System ◽  
Pedestrian Detection ◽  
Active Safety ◽  
Detection Accuracy ◽  
Cascade Algorithm ◽  
Automobile Safety ◽  
High Detection ◽  
Detection Effect

In order to improve automobile active safety performance, and reduce the traffic accidents between pedestrians and vehicles, a pedestrian detection method combined with pedestrian contour features is proposed based on the combination of the reliable Adaboost and SVM. For the requirements of fast and accurate pedestrian detection system, ten types of haar-like features are given as the coarse features firstly, and which are trained through Adaboost cascade algorithm to ensure the system with a high detection speed. Then, the hog features of strong ability to distinguish pedestrians are selected as the fine features, and the pedestrian classifier is got by using SVM of different kernels to improve the detection accuracy. It is shown that the method has a higher detection rate and achieves a better detection effect.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

Semi supervised inspection algorithm of automatic packaging curve based on deep learning

2021 ◽  
pp. 1-10
Author(s):  
Yong He
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Optimization Algorithm ◽  
Posterior Probability ◽  
Detection Method ◽  
Detection System ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Data Set ◽  
Packaging Process

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.

Detection of Orange Shape Structure Parameters Based on DSP

2011 ◽  
Vol 480-481 ◽  
pp. 17-20
Author(s):  
Li Zhu ◽  
Jia Quan Chen
Keyword(s):  
High Performance ◽  
Detection System ◽  
Fruit Shape ◽  
Detection Accuracy ◽  
Low Grade ◽  
Structure Parameters ◽  
Core Component ◽  
The Core ◽  
Shape Structure ◽  
High Detection

A Detection system of Orange shape structure parameters Based on DSP, including its hardware part and software part was introduced. The hardware part equipped with the high performance DSP acts as the core component of the image processing, which provides a guarantee of the real-time of the shape detection. The software part introduces the basic principle of the orange recognition arithmetic, and differentiates by Zernike moments and k-means algorithm. The experiments show it can meet the practical detection requirements that the high detection accuracy of the normal fruit shape and the low-grade fruit shape.

Fault Signal Detection System of Large Scale Integrated Circuit Based on Single Chip Microcomputer

2014 ◽  
Vol 716-717 ◽  
pp. 924-927
Author(s):  
Xiu Ying Li ◽  
Cun Ping Liu ◽  
Rong Fang Mei
Keyword(s):  
Signal Detection ◽  
Integrated Circuit ◽  
Large Scale ◽  
Detection Method ◽  
Detection System ◽  
Design Method ◽  
Detection Accuracy ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Working Performance

In the process of fault signal detection of large-scale integrated circuit, the fault signal detection can improve the working performance of large-scale integrated circuit. The traditional detection system is simple, time-consuming, and the error is large, the fault signal detection haslow accuracy. In view of this situation, a new design method of large scale integrated circuit fault signal detection system is proposed based on single chip microcomputer. S3C2410 is taken as hardware design basis, and the signal sensor is used, the software algorithm uses three B spline wavelet transform for ORB wave detection method. The detection is completed.The experiment experimental results show that the system can improve the detection accuracy greatly, and effectively improve the work efficiency.

On-Line Detection Method for Automobile Frame Side Rail Process Holes

2012 ◽  
Vol 616-618 ◽  
pp. 1993-1996
Author(s):  
Yu Zhuo Men ◽  
Hai Bo Yu ◽  
Hua Wang ◽  
Jin Gang Gao ◽  
Xin Pan
Keyword(s):  
Machine Vision ◽  
Detection Method ◽  
Detection System ◽  
Ccd Camera ◽  
Line Detection ◽  
Detection Accuracy ◽  
Side Rail ◽  
On Line ◽  
System Prototype

On-line detection method for automobile frame side rail process holes is proposed in this articled. It is achieved by virtue of machine vision technology detection method. Many images captured by CCD camera are processed and analyzed to finally complete the automatic detection of automobile chassis frame process holes. Machine vision technology is applied to achieve the on-line detection of machining quality of frame side rail mounting holes. The developed detection system prototype has very high detection accuracy.

scholarly journals Quality Detection System of Transparent Nonel Tubes Based on Image Processing

2015 ◽  
Vol 9 (1) ◽  
pp. 697-702
Author(s):  
Guodong Sun ◽  
Wei Xu ◽  
Lei Peng
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection System ◽  
Recognition Rate ◽  
Human Vision ◽  
Detection Methods ◽  
Detection Accuracy ◽  
External Diameter ◽  
Quality Detection

The traditional quality detection method for transparent Nonel tubes relies on human vision, which is inefficient and susceptible to subjective factors. Especially for Nonel tubes filled with the explosive, missed defects would lead to potential danger in blasting engineering. The factors affecting the quality of Nonel tubes mainly include the uniformity of explosive filling and the external diameter of Nonel tubes. The existing detection methods, such as Scalar method, Analysis method and infrared detection technology, suffer from the following drawbacks: low detection accuracy, low efficiency and limited detection items. A new quality detection system of Nonel tubes has been developed based on machine vision in order to overcome these drawbacks. Firstly the system architecture for quality detection is presented. Then the detection method of explosive dosage and the relevant criteria are proposed based on mapping relationship between the explosive dosage and the gray value in order to detect the excessive explosive faults, insufficient explosive faults and black spots. Finally an algorithm based on image processing is designed to measure the external diameter of Nonel tubes. The experiments and practical operations in several Nonel tube manufacturers have proved the defect recognition rate of proposed system can surpass 95% at the detection speed of 100m/min, and system performance can meet the quality detection requirements of Nonel tubes. Therefore this quality detection method can save human resources and ensure the quality of Nonel tubes.

scholarly journals DEFECTS DETECTION METHOD BASED ON K-MEANS WITH PRIOR KNOWLEDGE FOR BIOMASS PARTICLES

2020 ◽  
Vol 61 (2) ◽  
pp. 225-232 ◽  
Author(s):  
Wei Wang ◽  
Yuan Juan Gong
Keyword(s):  
Prior Knowledge ◽  
Detection Method ◽  
Vision System ◽  
Detection Algorithm ◽  
Biomass Energy ◽  
Detection Accuracy ◽  
Detection Time ◽  
Region Detection ◽  
High Detection ◽  
Biomass Particles

Biomass particle is one of the most important solid briquette fuels for agricultural and forestry biomass energy. Temperature, pressure, moisture and discharge holes are important factors to control biomass particle forming. The inappropriate setting of the parameters or blocking of the discharge hole will lead to the defects of the biomass particles, such as too short or poor roundness or pits or cracks. In order to detect these defects automatically, this paper proposes a method based on K-Means with prior knowledge. Firstly, the inner boundary tracking region detection algorithm and filling algorithm are combined to extract the regions in the backlight image. The regions are divided into debris, independent biomass particle regions and adhesive biomass particle regions. Secondly, K-Means with prior knowledge is used to segment the adhesive regions to get the independent biomass particle regions. Finally, the features of the biomass particles are extracted to judge the type of defects. The proposed method has been tested on images acquired from the vision system of the ring roller pellet mill. Experimental results show the efficiency of the proposed method in high detection accuracy and short detection time.

scholarly journals Immature Apple Detection Method Based on Improved Yolov3

2021 ◽  
Vol 1 (1) ◽  
pp. 9-13
Author(s):  
Zhongqiang Huang ◽  
Ping Zhang ◽  
Ruigang Liu ◽  
Dongxu Li
Keyword(s):  
Decision Making ◽  
Feasible Solution ◽  
Detection Method ◽  
Detection Accuracy ◽  
Data Set ◽  
Apple Industry ◽  
Backbone Network ◽  
Target Frame ◽  
Detection Speed ◽  
Detection Effect

The identification of immature apples is a key technical link to realize automatic real-time monitoring of orchards, expert decision-making, and realization of orchard output prediction. In the orchard scene, the reflection caused by light and the color of immature apples are highly similar to the leaves, especially the obscuration and overlap of fruits by leaves and branches, which brings great challenges to the detection of immature apples. This paper proposes an improved YOLOv3 detection method for immature apples in the orchard scene. Use CSPDarknet53 as the backbone network of the model, introduce the CIOU target frame regression mechanism, and combine with the Mosaic algorithm to improve the detection accuracy. For the data set with severely occluded fruits, the F1 and mAP of the immature apple recognition model proposed in this article are 0.652 and 0.675, respectively. The inference speed for a single 416×416 picture is 12 ms, the detection speed can reach 83 frames/s on 1080ti, and the inference speed is 8.6 ms. Therefore, for the severely occluded immature apple data set, the method proposed in this article has a significant detection effect, and provides a feasible solution for the automation and mechanization of the apple industry.

Research on Key Security Detection Method of Cross Domain Information Sharing Based on PKG Trust Gateway

2021 ◽  
Author(s):  
RunQi Li
Keyword(s):  
Information Sharing ◽  
Detection Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Detection Time ◽  
System Parameters ◽  
Management Platform ◽  
Cross Domain ◽  
Domain Information ◽  
Detection Effect

Aiming at the problems of low precision, long detection time and poor detection effect in current cross domain information sharing key security detection methods, a cross domain information sharing key security detection method based on PKG trust gateway is proposed. By analyzing bilinear pairing based on elliptic curve and identity based encryption scheme, according to the independent system parameters of PKG management platform, cross domain authentication access mechanism is proposed. PKG of different trust domains is used as the trust gateway for cross domain authentication. The key escrow problem of PKG of different trust domains is solved through key sharing, and the communication key agreement mechanism is established to mutually authenticate the user nodes in the trust domains with different system parameters. The formal description of the rule detection of cryptographic functions, parameters and other information, supported by the dynamic binary analysis platform pin, dynamically records the encryption and decryption process information during the operation of the program, and realizes cross domain information sharing key security detection through the design of correlation vulnerability detection algorithm. The experimental results show that the cross-domain information shared key security detection effect of the proposed method is better, which can effectively improve the detection accuracy and shorten the detection time.

Research on Technology of the Laser Designator Multi-Parameter Integrated Detection Method

2013 ◽  
Vol 552 ◽  
pp. 276-280
Author(s):  
Jin Song Wang ◽  
Jin Qiu Qi ◽  
Hao Zeng Wang ◽  
Jian Nan Deng ◽  
Zhi Yong An
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Detection Efficiency ◽  
Detection Accuracy ◽  
High Detection ◽  
Testing Technology ◽  
Beam Displacement ◽  
Focus Spot ◽  
Integrated Detection ◽  
Detection Principle

According to the state of testing technology for laser designator multi-parametric, a multi-parameter integrated detection method on the basis of optical collimation and digital image processing technology is proposed, and the way for the detection of multi-parameter characteristics and integrated detection is analyzed. By using the detection principle of large aperture lens focus spot method, the parameter measurements, such as the divergence angle of the laser designator beam, displacement amount of the light spot move, spot of adjustment range and deviation and the multi-axis consistency are measured. Simultaneously, the parameters of the sight line alteration of daylight aiming sight, the graduation precision can also be tested. By the analysis of experiment,the method has high detection accuracy and detection efficiency.

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