Research on the Cascade Vehicle Detection Method Based on CNN

Hu, Jianjun; Sun, Yuqi; Xiong, Songsong

doi:10.3390/electronics10040481

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…For pedestrian and vehicle detection, Hu et al [ 42 ] proposed a cascade vehicle detection method combining multi-feature fusion and convolutional neural network (CNN), which had good robustness in a complex driving environment. Cai et al [ 43 ] proposed a multi-scale CNN (MS-CNN) network that could detect vehicles of different scales by using the information from different feature map resolutions.…”

Section: Related Workmentioning

confidence: 99%

PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

Sun

Liu

et al. 2022

Sensors

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Pedestrian and vehicle detection plays a key role in the safe driving of autonomous vehicles. Although transformer-based object detection algorithms have made great progress, the accuracy of detection in rainy scenarios is still challenging. Based on the Swin Transformer, this paper proposes an end-to-end pedestrian and vehicle detection algorithm (PVformer) with deraining module, which improves the image quality and detection accuracy in rainy scenes. Based on Transformer blocks, a four-branch feature mapping model was introduced to achieve deraining from a single image, thereby mitigating the influence of rain streak occlusion on the detector performance. According to the trouble of small object detection only by visual transformer, we designed a local enhancement perception block based on CNN and Transformer. In addition, the deraining module and the detection module were combined to train the PVformer model through transfer learning. The experimental results show that the algorithm performed well on rainy days and significantly improved the accuracy of pedestrian and vehicle detection.

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Section: Related Workmentioning

confidence: 99%

PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

Sun

Liu

et al. 2022

Sensors

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“…CNN-based object detection models, a branch of AI, are divided into the 1-stage detector that performs detection and classification at once and the 2-stage detector that separates detection and classification, and various models such as simple CNN [29][30][31][32][33][34][35] and R-CNN [32,[36][37][38][39][40][41], YOLO [36][37][38]42,43] have been used, while new models such as lean CNN [44] and convolutional recurrent neural network (CRNN) [45] are also being developed.…”

Section: Related Workmentioning

confidence: 99%

“…CNN-based object detection has been conducted in various studies, such as modifying parameters or combining various analytical methods to improve accuracy and processing speed. Hu et al [29] proposed a cascade vehicle detection method that combined CNN and various methods such as LBP, Haar-like, and HOG to improve the accuracy of vehicle detection through cameras in complex weather conditions, and 97.32% recall in complex driving environments indicates that the algorithm has good robustness. Another fused CNN method, hybrid CRNN-based network intrusion detection system (HCRN-NIDS) [45], is a detection method used in the field of information security, which combines RNN with CNN, showing excellent performance in detecting both local features and temperature features.…”

Section: Related Workmentioning

confidence: 99%

The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

et al. 2021

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As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.

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“…MobileNet architecture [37] was applied to build the base network in the original Faster R-CNN [36] framework. Hu et al [38] combined multifeatured fusion and convolutional neural network to the vehicle detection method.…”

Section: Related Workmentioning

confidence: 99%

A Nighttime Vehicle Detection Method with Attentive GAN for Accurate Classification and Regression

Liu

Qiu

Wang

et al. 2021

Entropy

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Vehicle detection plays a vital role in the design of Automatic Driving System (ADS), which has achieved remarkable improvements in recent years. However, vehicle detection in night scenes still has considerable challenges for the reason that the vehicle features are not obvious and are easily affected by complex road lighting or lights from vehicles. In this paper, a high-accuracy vehicle detection algorithm is proposed to detect vehicles in night scenes. Firstly, an improved Generative Adversarial Network (GAN), named Attentive GAN, is used to enhance the vehicle features of nighttime images. Then, with the purpose of achieving a higher detection accuracy, a multiple local regression is employed in the regression branch, which predicts multiple bounding box offsets. An improved Region of Interest (RoI) pooling method is used to get distinguishing features in a classification branch based on Faster Region-based Convolutional Neural Network (R-CNN). Cross entropy loss is introduced to improve the accuracy of classification branch. The proposed method is examined with the proposed dataset, which is composed of the selected nighttime images from BDD-100k dataset (Berkeley Diverse Driving Database, including 100,000 images). Compared with a series of state-of-the-art detectors, the experiments demonstrate that the proposed algorithm can effectively contribute to vehicle detection accuracy in nighttime.

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Research on the Cascade Vehicle Detection Method Based on CNN

Cited by 11 publications

References 19 publications

PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

A Nighttime Vehicle Detection Method with Attentive GAN for Accurate Classification and Regression

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