A Vehicle Detection Technique Using Binary Images for Heterogeneous and Lane-Less Traffic

Satyanarayana, G. S. R.; Majhi, Sudhan; Das, Santos Kumar

doi:10.1109/tim.2021.3062412

Cited by 27 publications

(8 citation statements)

References 36 publications

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“…e number of all positive samples predicted by the model is TP + FP, and the proportion of correct positive samples is called precision, as shown in formula (5). e number of all positive samples in the validation set is TP + FN, and the proportion of predicted positive samples is called recall, as shown in formula (6).…”

Section: Performance Evaluation Of Proposed Workmentioning

confidence: 99%

“…Generally, autonomous vehicles use various sensors, such as cameras, lidar, and radar, to detect objects [ 5 ]. Some researchers [ 6 ] detect vehicles by extracting binary images from discrete sensor arrays, and some researchers [ 7 ] have achieved good results in the detection task in bad weather through the sensing method of radar and camera information fusion. Compared with other sensors, the camera is now more accurate and more cost-effective at detecting objects.…”

Section: Introductionmentioning

confidence: 99%

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A Real‐Time Object Detector for Autonomous Vehicles Based on YOLOv4

Wang

et al. 2021

Computational Intelligence and Neuroscience

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Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.

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Section: Performance Evaluation Of Proposed Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Real‐Time Object Detector for Autonomous Vehicles Based on YOLOv4

Wang

et al. 2021

Computational Intelligence and Neuroscience

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“…The results of this study are compared, with earlier works on heterogeneous less lane-disciplined traffic, to validate the performance, as shown in Table 11. Ali et al [25], which used data from the MILD sensor for similar traffic condition, and one of the most recent studies from similar traffic conditions, though using different sensor [12], are used in this comparative study.…”

Section: Selection Of Data Segment Lengthmentioning

confidence: 99%

“…While selecting a suitable sensing device, factors such as application, accuracy, and cost should be taken into account. Presently loop detectors and video cameras are in greater use worldwide for this purpose [11], [12], [13]. However, during congestion and bad weather condition, video cameras face severe difficulty as they are not robust concerning illumination and occlusions (when a small vehicle is behind a big vehicle) [12] [14].…”

Section: Introductionmentioning

confidence: 99%

“…Presently loop detectors and video cameras are in greater use worldwide for this purpose [11], [12], [13]. However, during congestion and bad weather condition, video cameras face severe difficulty as they are not robust concerning illumination and occlusions (when a small vehicle is behind a big vehicle) [12] [14]. Due to this, inductive loop detectors (ILD) are preferred as a sensing device, especially for conditions where occlusion is a serious issue.…”

Section: Introductionmentioning

confidence: 99%

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A Multivariate Analysis Framework for Vehicle Detection From Loop Data Under Heterogeneous and Less Lane Disciplined Traffic

2021

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Accurate vehicle detection in real-time is a challenging problem for engineers and researchers working in the field of transportation engineering all over the world. To detect the vehicle presence in the lane-based traffic, one of the widely used traffic detectors is inductive loop detectors (ILD). For less lane disciplined and heterogeneous traffic, researchers have suggested another traffic detector known as a multiple inductive loop detector (MILD) system. In order to extract the vehicle count from MILD data, it is required to detect vehicle presence and segment the signature of different vehicles. This work is focused on the automated processing of MILD signals to get total vehicle count information in real-time under heterogeneous and less-lane disciplined traffic conditions. This study proposes a multivariate data analysis framework for the detection and segmentation of vehicle signature from the acquired data, without significant manual intervention. The major challenge in this process is the coupling of the multi-dimensional loop data, due to cross-talk across the loops. To address this, principal component analysis (PCA) is used with the additional benefit of dimensionality reduction. Though PCA is a well-known method, its application to the current problem is not trivial and calls for tailoring of the method. Here, a new PC selection strategy suitable for data under consideration is proposed, as the traditional approach does not fit this application and tends to be low accuracy. Subsequently, the principal components are processed using a thresholdbased method, which uses the mean absolute deviation measure, to detect the vehicle presence. The results show that the developed algorithm with the proposed strategy for PC selection achieved an average vehicle count accuracy of 90.38 % whereas with the traditional approach the accuracy is 24.31 %.INDEX TERMS Vehicle detection, multiple inductive loop detectors (MILD), PCA, mean absolute deviation, multivariate analysis, intelligent transportation system (ITS).

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Toward a deep CNN and RS‐GOA framework for vehicle detection, traffic flow estimation, and optimal path selection from surveillance videos

Sasikala

Neelaveni

Jose

2023

Int J Communication

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SummarySeveral autonomous traffic monitoring systems have been created as a result of the growing number of vehicles in urban areas. Traffic surveillance systems that use roadside cameras, in particular, are becoming widely used for traffic management. For an efficient traffic control and vehicle navigation system, accurate traffic flow information must be obtained based on the vehicles detected in surveillance videos. However, vehicles of various scales are difficult to spot in traffic surveillance videos due to the presence of barricades, other vehicles, and the impact of poor lighting. Also, adverse weather conditions like snow, fog, and heavy rain diminish the visual quality of the surveillance footage. This paper proposes multi‐scale dense nested deep CNN (MSDN‐DCNN) and regional search grasshopper optimization algorithm (RS‐GOA) framework to accurately detect the vehicles, estimate the traffic flow, and find the optimal path with less travel time. First, the surveillance videos are pre‐processed, which includes frame conversion, redundancy removal, and image enhancement. The pre‐processed frames are given as input to the MSDN‐DCNN for multi‐scale vehicle detection. The detected results are used for vehicle counting and estimating the traffic flow. Finally, the optimal path is chosen based on the traffic flow information by using the RS‐GOA algorithm. The performance of the proposed method is compared with the existing vehicle detection and path selection techniques. The results illustrate that the proposed Deep CNN‐RS‐GOA framework has improved performance with high detection accuracy (91.03%), high speed (53.9 fps), less running time (1,000 ms), less travel time, and faster convergence.

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A Vehicle Detection Technique Using Binary Images for Heterogeneous and Lane-Less Traffic

Cited by 27 publications

References 36 publications

A Real‐Time Object Detector for Autonomous Vehicles Based on YOLOv4

A Real‐Time Object Detector for Autonomous Vehicles Based on YOLOv4

A Multivariate Analysis Framework for Vehicle Detection From Loop Data Under Heterogeneous and Less Lane Disciplined Traffic

Toward a deep CNN and RS‐GOA framework for vehicle detection, traffic flow estimation, and optimal path selection from surveillance videos

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