A Traffic Surveillance Multi-Scale Vehicle Detection Object Method Base on Encoder-Decoder

Hong, Feng; Lu, Changhua; Liu, Chun; Liu, Ruru; Ju, Wei

doi:10.1109/access.2020.2979260

Cited by 28 publications

(15 citation statements)

References 30 publications

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“…However, from the authors' point of view, the reviewed techniques that were used to improve robustness positively reflected on the accuracy. For example, [82,94,96,112] used techniques to make detection algorithms more robust to scale variance and they reported an increase in accuracy. This relationship also applies if robust techniques are used to detect objects that are affected by occlusion, severe illumination and weather conditions.…”

Section: Discussionmentioning

confidence: 99%

“…RRC has the best result in the KITTI moderate and hard categories for the 2D monocular vision system. The following works aimed to handle scale sensitivity problem [82,94,96,112]. Cai et al [94] implemented a network called multi-scale CNN (MS-CNN) where they were able to detect vehicles at different scales by using information from different features maps resolution.…”

Section: Techniquesmentioning

confidence: 99%

“…Hong et al [112] modified the YOLOv3 algorithm by presenting a new multi-level feature pyramid (FPN). As illustrated in Figure 16, the multi level feature pyramid has three modules, the feature stitching, encoder-decoder and the feature fusion.…”

Section: Techniquesmentioning

confidence: 99%

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Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Galvao

Abbod

Kalganova

et al. 2021

Sensors

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Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

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Section: Discussionmentioning

confidence: 99%

Section: Techniquesmentioning

confidence: 99%

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Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Galvao

Abbod

Kalganova

et al. 2021

Sensors

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“…This dataset has now become an internationally used algorithm evaluation dataset for autonomous driving scenarios. KITTI dataset mainly focuses on performance evaluation of various computer vision technologies, including optical flow, stereo image, visual ranging, and object detection [38,39]. This dataset covers real road images in several scenarios, such as cities, villages, and highways.…”

Section: Kitti Datasetmentioning

confidence: 99%

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

Cao

Song

et al. 2020

Sensors

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Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people’s lives and property.

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“…Kim [17] et al introduced spatial pyramid pooling (SPP) into YoLoV3 and add more prediction layers in it, making it better fit to multi-scale vehicle detection in traffic surveillance and alleviate scale-sensitive problems. Another effort to mitigate scale-sensitive problems, especially for small vehicle target, Hong [29] et al modified YoLoV3 to a new pyramid structure based on codec module, which can achieve good performance in actual vehicle detection demand. Sentas [30] et al tried to introduce Tiny-YoLo into real-time vehicle detection field, and built TPSdataset for test.…”

Section: The Related Workmentioning

confidence: 99%

A Robust Vehicle Detection Scheme for Intelligent Traffic Surveillance Systems in Smart Cities

et al. 2020

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Accurately obtaining road vehicle information is important in intelligent traffic surveillance systems for smart cities. Especially smart vehicle detection is recognized as the critical research issue of intelligent traffic surveillance systems. In this paper, a robust real-time vehicle detection method for the system is proposed. The method combines background subtraction model MOG2(Mixture of Gaussians) with a modified SqueezeNet model (H-SqueezeNet). The MOG2 model is utilized to create scale-insensitive Region of Interest (RoIs) from video frames. H-SqueezeNet is then proposed to accurately identify vehicle category. The effectiveness of the method was verified in CDnet2014 dataset, UA-DETRAC dataset and video data from a traffic intersection in Suzhou, China. The experiment results show that the method can achieves excellent detection accuracy in traffic surveillance systems, and achieve an average detection speed of 39.1 FPS.

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A Traffic Surveillance Multi-Scale Vehicle Detection Object Method Base on Encoder-Decoder

Cited by 28 publications

References 30 publications

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Front Vehicle Detection Algorithm for Smart Car Based on Improved SSD Model

A Robust Vehicle Detection Scheme for Intelligent Traffic Surveillance Systems in Smart Cities

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