A Highly Efficient Vehicle Taillight Detection Approach Based on Deep Learning

Li, Qiaohong; Garg, Sahil; Nie, Jiangtian; Li, Xiang; Liu, Ryan Wen; Cao, Zhiguang; Hossain, M. Shamim

doi:10.1109/tits.2020.3027421

Cited by 35 publications

(25 citation statements)

References 39 publications

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“…Due to the different feature information extracted by convolutional layers of different depths, the feature fusion method was born in order to enhance the network expression ability. References [7][8][9][10] combine feature fusion methods with object detection networks. By fusing the object feature information extracted from different levels, the ability of the network to learn shallow detail information or deep semantic information is enhanced, so as to improve the multi-scale detection accuracy of the network for complex shape objects.…”

Section: Related Workmentioning

confidence: 99%

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MPGI‐Terminal defect detection based on M‐FRCNN

Liu

Wang

et al. 2022

IET Image Processing

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Micro‐precision glass insulated terminal (MPGI‐Terminal) is a high‐precision and widely used micro‐precision component. The MPGI‐Terminal is small in size with 2–5 mm in diameter. It is difficult to distinguish missing blocks on the surface during manual inspection, which is prone to false detection and missing detection. The advantages of deep learning methods in the field of object detection have gradually highlighted. They can be used to construct the missing block detection model of MPGI‐Terminal. A Max‐Min Intersection over Union (MIoU) loss function based on overlap area, distance and aspect ratio is proposed, which helps the model to perform bounding box regression more reasonably, so as to obtain the object position more accurately. In addition, the Spatial Attention Module (SAM) and the Region of Interest Alignment (RoI Align) module are introduced to further improve the model performance. Based on the above modules, a MPGI‐Terminal missing block detection model based on M‐FRCNN is constructed. The results show that the proposed model can meet the requirements of fast and high accuracy of MPGI‐Terminal missing block detection.

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Section: Related Workmentioning

confidence: 99%

“…where h max = max(h gt , h), h min = min(h gt , h), w max = max(w gt , w), w min = min(w gt , w). Therefore, the expression of the MIoU loss function is shown in (8).…”

Section: Proposed Miou Loss Functionmentioning

confidence: 99%

MPGI‐Terminal defect detection based on M‐FRCNN

Liu

Wang

et al. 2022

IET Image Processing

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“…Zhang et al [55] proposed a new platform called Caffeine for FPGA hardware, and the execution speed was improved 9 29 in performance. [25] and [6] focus their research on vehicle detection which has a key impact in autonomous driving field. Both, based their development in YOLOv3-based algorithm achieving improvements in the accuracy of at least 7% more.…”

Section: Convolutional Neural Network Improvementsmentioning

confidence: 99%

A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3

Martinez‐Alpiste

Golkarnarenji

Wang

et al. 2021

Neural Comput & Applic

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This paper proposes an acceleration technique to minimise the unnecessary operations on a state-of-the-art machine learning model and thus to improve the processing speed while maintaining the accuracy. After the study of the main bottlenecks that negatively affect the performance of convolutional neural networks, this paper designs and implements a discarding technique for YOLOv3-based algorithms to increase the speed and maintain accuracy. After applying the discarding technique, YOLOv3 can achieve a 22% of improvement in terms of speed. Moreover, the results of this new discarding technique were tested on Tiny-YOLOv3 with three output layers on an autonomous vehicle for pedestrian detection and it achieved an improvement of 48.7% in speed. The dynamic discarding technique just needs one training process to create the model and thus execute the approach, which preserves accuracy. The improved detector based on the discarding technique is able to readily alert the operator of the autonomous vehicle to take the emergency brake of the vehicle in order to avoid collision and consequently save lives.

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“…D. Ava et al [2] proposed a target detection model based on YOLO and realized the brake light detection and classification algorithm by using support vector machine classifier SVM. Qiaohong Li et al [3] adopted fast Yolov3-Tiny [4] model to locate braking and steering signals from video stream in real time. F.i.vancea et al [5] proposed a convolutional neural network architecture based on Faster RCNN [6] to segment taillight pixels and identify the signal status of taillight by detecting vehicles.…”

Section: Introductionmentioning

confidence: 99%

Using attention mechanism and improved YOLOX vehicle taillight detection

Zhao

Zhang

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Taillight detection is of great significance and value in predicting driving intention of vehicle ahead in assisted driving and unmanned driving systems. Aiming at the problem that the vehicle taillight detection model YOLOX is not able to detect small targets and the channel information does not contain location information, a taillight detection algorithm based on the attention mechanism and improved YOLOX is proposed. Firstly, a micro-scale detection layer is added to further multi-scale feature fusion to extract more taillight feature information. Secondly, the mobile network attention mechanism is inserted into the proposed improved feature fusion network to extract taillight position information. Finally, the sensing field area at the output end of feature extraction layer is enlarged to improve the detection accuracy of small targets such as taillights. The taillight data set established in natural environment was used, and the number of samples was 1,962 images. The training set, verification set and test set were randomly divided in 8:1:1 ratio to verify the improved network. Experimental results show that the improved model has a 91.71% average detection accuracy (mAP) for taillight images, which is 3.48% higher than the standard YOLOX algorithm.

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A Highly Efficient Vehicle Taillight Detection Approach Based on Deep Learning

Cited by 35 publications

References 39 publications

MPGI‐Terminal defect detection based on M‐FRCNN

MPGI‐Terminal defect detection based on M‐FRCNN

A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3

Using attention mechanism and improved YOLOX vehicle taillight detection

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