Vehicle pose detection using region based convolutional neural network

Azam, Shoaib; Rafique, Aasim; Jeon, Moongu

doi:10.1109/iccais.2016.7822459

Cited by 16 publications

(8 citation statements)

References 19 publications

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“…Fast R-CNN [8] improved on R-CNN by using a feature extractor (CNN) to extract features over the whole image thereby speeding up the training and inference process. Faster R-CNN [9] further improved the training and inference speed and proved to be usable for real-time vehicle detection in reference [10]. Redmon et al introduced a single shot detector model YOLO in 2016 [11] which further greater reduced the speed of detection and improved the accuracy.…”

Section: Related Workmentioning

confidence: 99%

PyTorch YOLOv3 Object Detection for Vehicle Identification

2020

IJRTE

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Detecting real-world vehicle objects captured from car-mounted cameras requires manual labelling of video images. Previous vehicle object detection papers such as the winners of the 2018 AI City Challenge [1] used a training set of over 4,500 hand labelled images. In this paper, we attempt to automate this task by applying transfer learning to a YOLOv3 model trained on Imagenet and then re-trained on a set of stock car images and a small subset of hand labelled images taken from front-mounted dashboard camera videos. The mean Average Precision (mAP) of the validation set is used to determine the effectiveness of model vehicle classification. There is a significant variance issue between the validation and training set because the video images are taken in 1) various weather and lighting conditions and 2) the stock images have different image perspectives. The experimental results demonstrate that the YOLOv3 model can reach an overall 16.07% mAP after 60 epochs of training and can identify classes of vehicles that had few training examples in the dataset

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Section: Related Workmentioning

confidence: 99%

PyTorch YOLOv3 Object Detection for Vehicle Identification

2020

IJRTE

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“…The monitoring system is used for traffic flow systematic management generally on the roads, such as traffic flow analysis system, license plate detection, highway toll system according to vehicle types and behavior detection of illegal traffic, etc. [1]. This field has been studied actively over the past decades.…”

Section: Introductionmentioning

confidence: 99%

Multi-object Recognition Method Based on Improved YOLOv2 Model

Shi

Li²,

Tang³

et al. 2021

ITC

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A method of vehicle multi-object identification and classification based on the YOLOv2 algorithm is proposed to solve the problems of low detection rate, poor robustness, and unsatisfactory classification effect for the classical multi-object detection and vehicle type classification on real road environment. Based on the YOLOv2 algorithm, the network structure of YOLOv2-voc is improved according to the actual road conditions. The classification training model was obtained based on the ImageNet data and fine-tuning technology, according to the analysis of training results and vehicle object characteristics. This paper proposed the improved vehicle identification classification network structure, namely called YOLOv2-voc_mul. In order to verify the validity of the detection method, experiments are performed using samples from simple backgrounds and complex backgrounds and compared with the existing YOLOv2, YOLOv2-voc, and YOLOv3 models after 70000 iterations, respectively. The results show that the proposed YOLOv2-voc_mul model has an accuracy of 98.6% under the simple background, and the mAP (mean Average Precision) of different models reaches 87.81%. Under the complex background, the improved YOLOv2-voc_mul model has an average accuracy of 92.09% and 89.64% for single and multi-object detection of four different models. In summary, our proposed method has better accuracy, a low false detection rate, and good robustness.

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“…Furthermore, the MS-CNN can conduct detection with at a rate of frames per second. References [20,21] applied the Faster R-CNN-based method to vehicle detection, and achieved good detection. Reference [22] combined Faster R-CNN, VGG16, and ResNet-152 for vehicle detection, which achieved good vehicle detection accuracy, although the speed was slow and could not satisfy the requirements for real-time vehicle detection.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv2 for Vehicle Detection

Sang

Guo

et al. 2018

Sensors

160

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Vehicle detection is one of the important applications of object detection in intelligent transportation systems. It aims to extract specific vehicle-type information from pictures or videos containing vehicles. To solve the problems of existing vehicle detection, such as the lack of vehicle-type recognition, low detection accuracy, and slow speed, a new vehicle detection model YOLOv2_Vehicle based on YOLOv2 is proposed in this paper. The k-means++ clustering algorithm was used to cluster the vehicle bounding boxes on the training dataset, and six anchor boxes with different sizes were selected. Considering that the different scales of the vehicles may influence the vehicle detection model, normalization was applied to improve the loss calculation method for length and width of bounding boxes. To improve the feature extraction ability of the network, the multi-layer feature fusion strategy was adopted, and the repeated convolution layers in high layers were removed. The experimental results on the Beijing Institute of Technology (BIT)-Vehicle validation dataset demonstrated that the mean Average Precision (mAP) could reach 94.78%. The proposed model also showed excellent generalization ability on the CompCars test dataset, where the “vehicle face” is quite different from the training dataset. With the comparison experiments, it was proven that the proposed method is effective for vehicle detection. In addition, with network visualization, the proposed model showed excellent feature extraction ability.

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Vehicle pose detection using region based convolutional neural network

Cited by 16 publications

References 19 publications

PyTorch YOLOv3 Object Detection for Vehicle Identification

PyTorch YOLOv3 Object Detection for Vehicle Identification

Multi-object Recognition Method Based on Improved YOLOv2 Model

An Improved YOLOv2 for Vehicle Detection

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