Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance

Wei, Jian; Jiang, He; Zhou, Yi; Chen, Kai; Tang, Zuoyin; Xiong, Zhiliang

doi:10.1109/tits.2019.2910643

Cited by 97 publications

(39 citation statements)

References 33 publications

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“…Some works have been done to deal with scale problems in vehicle detection. For example, a unified deep neural network [28], named the multi-scale CNN, was proposed to detect objects of different scales; Hu et al [29] proposed a scale-insensitive convolutional neural network to overcome the scale-sensitive problem of CNN models in vehicle detection; Wei et al [30] offered three enhancements on a multiple scale CNN network for visual detection in advanced driving assistance systems. For some particular tasks or scenarios, designing completely new networks are also required.…”

Section: B Deep Learningmentioning

confidence: 99%

A Lightweight CNN Model for Refining Moving Vehicle Detection From Satellite Videos

Chen

2020

IEEE Access

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With the recent development of earth observation technology, the satellites have obtained the ability to capture city-scale videos, which enable potential applications in intelligent traffic management. Because of the broad field-of-view, the moving vehicles in satellite videos are usually composed of only tens of pixels, making it difficult to differentiate true objects from noise and other distractors. In addition, the edges of tall building tops are often mistakenly detected as moving vehicles because of the effects of motion parallax. This paper proposed a terse framework that can effectively suppress false targets, achieving high precision and recall. The study involves three parts: 1) An adaptive filtering method is proposed to reduce noise, thus making the detection algorithm more reliable; 2) Several background subtraction models are tested, and the best one is chosen to produce the preliminary detection results at high recall but low accuracy; 3) A lightweight convolutional neural network (LCNN) is designed and trained on a small collection of samples, and then used to eliminate false targets. The experiments and evaluations demonstrate that our method can largely improve the precision at the expense of a slight reduction of recall. INDEX TERMS vehicle detection, object detection, convolutional neural network, background subtraction model, satellite video

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Section: B Deep Learningmentioning

confidence: 99%

A Lightweight CNN Model for Refining Moving Vehicle Detection From Satellite Videos

Chen

2020

IEEE Access

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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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“…The SINet contains a context-aware RoI pooling layer and a multi-branch decision network for vehicle detection, which can produce accurate feature maps for vehicles with small scales and classify vehicles with a large variance of scales. Wei et al [35] present a multiple scale CNN network model for advanced driving assistance systems object detection. In this study, the deconvolution and fusion of CNN feature maps are proposed to add context and deeper features for better object detection at lower scale of feature maps, and the experimental results demonstrate the effectiveness of the proposed method with good detection performance over KITTI [36] test set.…”

Section: The General Vehicle Detection Algorithmsmentioning

confidence: 99%

Vehicle Detection in Aerial Images Based on Lightweight Deep Convolutional Network and Generative Adversarial Network

et al. 2019

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Vehicle detection in aerial images is a challenging task and plays an important role in a wide range of applications. Traditional detection algorithms are based on sliding-window searching and shallowlearning-based features, which limits the ability to represent features and generates a lot of computational costs. Recently, with the successful application of convolutional neural network in computer vision, many state-of-the-art detectors have been developed based on deep CNNs. However, these CNN-based models still face some difficulties and challenges in vehicle detection in aerial images. Firstly, the CNN-based detection model requires extensive calculations during training and detection, and the accuracy of detection for small objects is not high. In addition, deep learning models often require a large amount of sample data to train a robust detection model, while the annotated data of aerial vehicles is limited. In this study, we propose a lightweight deep convolutional neural network detection model named LD-CNNs. The detection algorithm not only greatly reduces the computational costs of the model, but also significantly improves the accuracy of the detection. What's more, in order to cope with the problem of insufficient training samples, we develop a multi-condition constrained generative adversarial network named MC-GAN, which can effectively generate samples. The detection performance of the proposed model has been evaluated on the Munich public dataset and the collected dataset respectively. The results show that on the Munich dataset, the proposed method achieves 86.9% on mAP (mean average precision), F1-score is 0.875, and the detection time is 1.64s on Nvidia Titan XP. At present, these detection indicators have reached state-of-the-art level in vehicle detection of aerial images. INDEX TERMS Vehicle detection, lightweight convolutional network, generative adversarial network, aerial images.

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Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance

Cited by 97 publications

References 33 publications

A Lightweight CNN Model for Refining Moving Vehicle Detection From Satellite Videos

A Lightweight CNN Model for Refining Moving Vehicle Detection From Satellite Videos

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

Vehicle Detection in Aerial Images Based on Lightweight Deep Convolutional Network and Generative Adversarial Network

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