scholarly journals Robust Vehicle Speed Measurement Based on Feature Information Fusion for Vehicle Multi-Characteristic Detection

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 910
Author(s):  
Lei Yang ◽  
Jianchen Luo ◽  
Xiaowei Song ◽  
Menglong Li ◽  
Pengwei Wen ◽  
...  
Keyword(s):  
Measurement Error ◽  
Object Detection ◽  
Information Fusion ◽  
Measurement System ◽  
Error Rate ◽  
Detection Algorithm ◽  
Vehicle Speed ◽  
Channel Interaction ◽  
Speed Measurement ◽  
Feature Information

A robust vehicle speed measurement system based on feature information fusion for vehicle multi-characteristic detection is proposed in this paper. A vehicle multi-characteristic dataset is constructed. With this dataset, seven CNN-based modern object detection algorithms are trained for vehicle multi-characteristic detection. The FPN-based YOLOv4 is selected as the best vehicle multi-characteristic detection algorithm, which applies feature information fusion of different scales with both rich high-level semantic information and detailed low-level location information. The YOLOv4 algorithm is improved by combing with the attention mechanism, in which the residual module in YOLOv4 is replaced by the ECA channel attention module with cross channel interaction. An improved ECA-YOLOv4 object detection algorithm based on both feature information fusion and cross channel interaction is proposed, which improves the performance of YOLOv4 for vehicle multi-characteristic detection and reduces the model parameter size and FLOPs as well. A multi-characteristic fused speed measurement system based on license plate, logo, and light is designed accordingly. The system performance is verified by experiments. The experimental results show that the speed measurement error rate of the proposed system meets the requirement of the China national standard GB/T 21555-2007 in which the speed measurement error rate should be less than 6%. The proposed system can efficiently enhance the vehicle speed measurement accuracy and effectively improve the vehicle speed measurement robustness.

scholarly journals Object Detection Algorithm Based on Multiheaded Attention

2019 ◽  
Vol 9 (9) ◽  
pp. 1829 ◽  
Author(s):  
Jie Jiang ◽  
Hui Xu ◽  
Shichang Zhang ◽  
Yujie Fang
Keyword(s):  
Object Detection ◽  
Linear Interpolation ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Visual Object ◽  
Single Shot ◽  
Object Class ◽  
Feature Information ◽  
Base Network ◽  
Detector Model

This study proposes a multiheaded object detection algorithm referred to as MANet. The main purpose of the study is to integrate feature layers of different scales based on the attention mechanism and to enhance contextual connections. To achieve this, we first replaced the feed-forward base network of the single-shot detector with the ResNet–101 (inspired by the Deconvolutional Single-Shot Detector) and then applied linear interpolation and the attention mechanism. The information of the feature layers at different scales was fused to improve the accuracy of target detection. The primary contributions of this study are the propositions of (a) a fusion attention mechanism, and (b) a multiheaded attention fusion method. Our final MANet detector model effectively unifies the feature information among the feature layers at different scales, thus enabling it to detect objects with different sizes and with higher precision. We used the 512 × 512 input MANet (the backbone is ResNet–101) to obtain a mean accuracy of 82.7% based on the PASCAL visual object class 2007 test. These results demonstrated that our proposed method yielded better accuracy than those provided by the conventional Single-shot detector (SSD) and other advanced detectors.

scholarly journals Estimation of optical speed measurement error for traffic monitoring systems

2021 ◽  
Vol 244 ◽  
pp. 08023
Author(s):  
Iaroslav Khutornoi
Keyword(s):  
Measurement Error ◽  
Measurement Accuracy ◽  
Traffic Monitoring ◽  
Monitoring Systems ◽  
License Plate ◽  
Vehicle Speed ◽  
Video Images ◽  
Speed Measurement ◽  
Plate Size ◽  
Camera Parameters

This article is devoted to the problem of accurate vehicle speed measurement from video images. The analysis of existing methods of optical speed measurement is carried out, and the need to estimate the measurement error is justified. An idealized mathematical model of speed measurement based on binding to the size of the license plate is studied and the influence of various factors on the measurement accuracy is shown: the influence of the intrinsic and extrinsic camera parameters, the influence of license plate size in images, influence of data averaging. A comparison of the obtained results with the accuracy of widely used in practice radar technologies is shown.

scholarly journals An Improved Stereo Matching Algorithm for Vehicle Speed Measurement System Based on Spatial and Temporal Image Fusion

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 866
Author(s):  
Lei Yang ◽  
Qingyuan Li ◽  
Xiaowei Song ◽  
Wenjing Cai ◽  
Chunping Hou ◽  
...  
Keyword(s):  
Image Fusion ◽  
Measurement System ◽  
Stereo Matching ◽  
License Plate ◽  
Vehicle Speed ◽  
Matching Algorithm ◽  
Speed Measurement ◽  
Optimum Speed ◽  
Binocular Stereo ◽  
Plate Area

This paper proposes an improved stereo matching algorithm for vehicle speed measurement system based on spatial and temporal image fusion (STIF). Firstly, the matching point pairs in the license plate area with obviously abnormal distance to the camera are roughly removed according to the characteristic of license plate specification. Secondly, more mismatching point pairs are finely removed according to local neighborhood consistency constraint (LNCC). Thirdly, the optimum speed measurement point pairs are selected for successive stereo frame pairs by STIF of binocular stereo video, so that the 3D points corresponding to the matching point pairs for speed measurement in the successive stereo frame pairs are in the same position on the real vehicle, which can significantly improve the vehicle speed measurement accuracy. LNCC and STIF can be used not only for license plate, but also for vehicle logo, light, mirror etc. Experimental results demonstrate that the vehicle speed measurement system with the proposed LNCC+STIF stereo matching algorithm can significantly outperform the state-of-the-art system in accuracy.

An FPGA-based vehicle speed measurement system using an uncalibrated camera

ICCAS 2010 ◽  
2010 ◽  
Author(s):  
Ji Ho Song ◽  
Nguyen Tuong Thuy ◽  
Seunghun Jin ◽  
Dongkyun Kim ◽  
Jae Wook Jeon
Keyword(s):  
Measurement System ◽  
Vehicle Speed ◽  
Uncalibrated Camera ◽  
Speed Measurement

scholarly journals An FPGA-Based Ultra-High-Speed Object Detection Algorithm with Multi-Frame Information Fusion

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3707 ◽  
Author(s):  
Xianlei Long ◽  
Shenhua Hu ◽  
Yiming Hu ◽  
Qingyi Gu ◽  
Idaku Ishii
Keyword(s):  
Object Detection ◽  
Information Fusion ◽  
High Speed ◽  
Clock Cycle ◽  
Image Data ◽  
Detection Algorithm ◽  
Support Vector ◽  
Fusion Model ◽  
Ultra High Speed ◽  
Field Programmable

An ultra-high-speed algorithm based on Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) for hardware implementation at 10,000 frames per second (FPS) under complex backgrounds is proposed for object detection. The algorithm is implemented on the field-programmable gate array (FPGA) in the high-speed-vision platform, in which 64 pixels are input per clock cycle. The high pixel parallelism of the vision platform limits its performance, as it is difficult to reduce the strides between detection windows below 16 pixels, thus introduce non-negligible deviation of object detection. In addition, limited by the transmission bandwidth, only one frame in every four frames can be transmitted to PC for post-processing, that is, 75% image information is wasted. To overcome the mentioned problem, a multi-frame information fusion model is proposed in this paper. Image data and synchronization signals are first regenerated according to image frame numbers. The maximum HOG feature value and corresponding coordinates of each frame are stored in the bottom of the image with that of adjacent frames’. The compensated ones will be obtained through information fusion with the confidence of continuous frames. Several experiments are conducted to demonstrate the performance of the proposed algorithm. As the evaluation result shows, the deviation is reduced with our proposed method compared with the existing one.

Compensating Background for Noise due to Camera Vibration in Uncalibrated-Camera-Based Vehicle Speed Measurement System

2011 ◽  
Vol 60 (1) ◽  
pp. 30-43 ◽  
Author(s):  
Thuy Tuong Nguyen ◽  
Xuan Dai Pham ◽  
Ji Ho Song ◽  
Seunghun Jin ◽  
Dongkyun Kim ◽  
...  
Keyword(s):  
Measurement System ◽  
Vehicle Speed ◽  
Uncalibrated Camera ◽  
Speed Measurement

scholarly journals Novel Joint Object Detection Algorithm Using Cascading Parallel Detectors

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 137
Author(s):  
Zihan Zhou ◽  
Qinghan Lai ◽  
Shuai Ding ◽  
Song Liu
Keyword(s):  
Object Detection ◽  
Information Fusion ◽  
Spatial Information ◽  
Prediction Method ◽  
Detection Algorithm ◽  
Free Module ◽  
High Quality ◽  
Shape Prediction ◽  
Proposed Model ◽  
Open Datasets

Object detection is an essential computer vision task that aims to detect target objects from an image. The traditional models are insufficient to generate a high-quality anchor box. To solve the problem, we propose a novel joint model called guided anchoring Region proposal networks and Cascading Grid Region Convolutional Neural Networks (RCGrid R-CNN), enhancing the ability of object detection. Our proposed model design is a joint object detection algorithm containing an anchor-based and an anchor-free branch in parallel and symmetry. In the anchor-based, we use nine-point spatial information fusion to obtain better anchor box location and introduce the shape prediction method of Guided Anchoring Region Proposal Networks (GA-RPN) to enhance the accuracy of the predicted anchor box. In the anchor-free branch, we introduce the Feature Selective Anchor-Free module (FSAF) to reduce the overlapping anchor boxes to obtain a more accurate anchor box. Furthermore, inspired by cascading theory, we cascade the new-designed detectors to improve the ability of object detection by setting a gradually increasing Intersection over Union (IoU) threshold. Compared with typical baseline models, we comprehensively evaluated our model by conducting experiments on two open datasets: Pascal VOC2007 and COCO2017. The experimental results demonstrate the effectiveness of RCGrid R-CNN in producing a high-quality anchor box.

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