EnsembleNet: a hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models

Mittal, Usha; Chawla, Priyanka; Tiwari, Rajeev

doi:10.1007/s00521-022-07940-9

Cited by 75 publications

(30 citation statements)

References 51 publications

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“…This is confirmed by Mittal et al. (2023), who reported a performance comparison between YOLOv5 and Faster R‐CNN for vehicle detection tasks as generally accepted. Since the proposed model uses still images as input and aims for dimensional measurement, it prioritizes detection accuracy over processing speed.…”

Section: Automated Cross‐sectional Reconstruction Modelsupporting

confidence: 71%

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Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Park

2023

Computer aided Civil Eng

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This study presents a novel, deep‐learning‐based model for the automated reconstruction of a cross‐sectional drawing from stereo photographs. Targeted cross‐sections captured in stereo photographs are detected and translated into sectional drawings using faster region‐based convolutional neural networks and Pix2Pix generative adversarial network. To address the challenge of perspective correction in the photographs, a novel camera pose optimization method is introduced and employed. This method eliminates the need for camera calibration and image matching, thereby offering greater flexibility in camera positioning and facilitating the use of telephoto lenses while avoiding image‐matching errors. Moreover, synthetic image datasets are used for training to facilitate the practical implementation of the proposed model in construction industry applications, considering the limited availability of open datasets in this field. The applicability of the proposed model was evaluated through experiments conducted on the cross‐sections of curtain wall components. The results demonstrated superior measurement accuracy, compared with those of current methods of laser scanning or camera‐based measurements for construction components.

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Section: Automated Cross‐sectional Reconstruction Modelsupporting

confidence: 71%

“…This is due to its superior detection accuracy, despite having a slower data processing speed, compared to YOLO. This is confirmed by Mittal et al (2023), who reported a performance comparison between YOLOv5 and Faster R-CNN for vehicle detection tasks as generally accepted.…”

Section: Detection Of the Target Cross-sectionsupporting

confidence: 64%

Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Park

2023

Computer aided Civil Eng

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“…A gray convolutional neural network (G‐CNN) was proposed by Mittal et al 35 for traffic flow prediction. Initially, due to the small sample size and incomplete information description of traffic accidents, the traffic speed and traffic flow change rate were defined to represent the gray information of traffic accidents.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…Wang Liang [3] et al proposed a fast detection method about unmanned boat targets at sea by utilizing deep learning advantages and designing a multi-source and multi-institution cooperative sensing architecture. Mittal [4] et al proposed a vehicle detection based on Faster R-CNN and YOLOv5 with majority voting classifier based on thermal images and visible light images collected from different sources , which effectively improves road traffic management.…”

Section: Introductionmentioning

confidence: 99%

Yangtze River inland waterway bulk carrier sealing status recognition on improved YOLOv5 algorithm

Xiong,

Zhang,

Xiao

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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In order to further respond to the national policy and improve the monitoring capability of the sealing status of inland bulk carriers on the Yangtze River, this paper proposes the C-YOLOv5 model on the basis of the YOLOv5 algorithm. The elkan K-Means clustering algorithm is introduced to optimize the target candidate frame so that it can adapt to the detection environment of small targets while ensuring the recall and accuracy of recognition. In order to improve the focus on the detected targets, the SE focus mechanism module is introduced in the head of the model. In addition, some of the ordinary convolutions in the structure are replaced with depth-separable convolutions to further improve the detection speed. The experimental results show that the detection accuracy and speed are improved by using the C-YOLOv5 model, with an accuracy as high as 91.1% and a speed as high as 66.5 f/s. The detection accuracy and retrieval rate of the C-YOLOv5 algorithm both reach 90%. The C-YOLOv5 algorithm's ability to recognize the target state is also significantly improved under poor line-of-sight and dark light test conditions.

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EnsembleNet: a hybrid approach for vehicle detection and estimation of traffic density based on faster R-CNN and YOLO models

Cited by 75 publications

References 51 publications

Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

Automated reconstruction model of a cross‐sectional drawing from stereo photographs based on deep learning

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

Yangtze River inland waterway bulk carrier sealing status recognition on improved YOLOv5 algorithm

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