A Safety Vehicle Detection Mechanism Based on YOLOv5

Huang, Yeting; Zhang, Hancui

doi:10.1109/smartcloud52277.2021.00008

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…The total number of acquired images was 7500 images. The experimental results showed 0.924 mean average precision [15]. The dataset is specific to Iraq streets; only mean average precision is reported.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition

Saba Ramzan,

Muhammad Javaid Iqbal,

Farrukh Arslan

et al. 2024

JICET

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Secure intelligent parking systems are complicated issues around the world. A secure parking system that is larceny-free is considered a luxury now. Parking is expensive and limited in almost all large cities. This research study presents a unique, safe, intelligent parking system based on deep image recognition techniques. To solve this tedious problem, an object detection deep learning model such as You Only Look Once (YOLO) is used. The proposed model is divided into three steps. The first step captures the car's image while entering the parking area. The second step is License plate recognition, in which the license plate is detected and saved the individual characters, and the recognized dataset is used when the driver leaves the parking area. The third step is facing detection and recognition of the taken plate. The fourth step is that the printer generates an invoice code used in case of a mismatch in the face of the driver. Evaluation of the proposed model indicates its efficiency. Accuracy of the following modules is achieved for vehicle detection (99%), number plate detection and recognition 98%, and 95%, respectively, and face detection and recognition 99%, and 97%, respectively. The proposed model's efficiency is shown through its evaluation. To get more security, a two-way screening procedure is used to keep the vehicles secure. So, the identification system's unique idea is to achieve better results by using two cameras and two-way authentication. It also forbids the entrance of those vehicles and drivers who are unauthorized or not allowed by the firm. Moreover, these modules were successfully implemented and evaluated using various performance measures, including precision, recall, and mean average precision.

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Section: Literature Reviewmentioning

confidence: 99%

Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition

Saba Ramzan,

Muhammad Javaid Iqbal,

Farrukh Arslan

et al. 2024

JICET

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show abstract

“…In Figure 9, the forms and dimensions of 10 predicted anchor boxes based on the proposed dataset are shown. The values of these anchor boxes calculated via K-means++ clustering are as follows: (12,21), (15,34), (20,49), (30,60), (35,77), (40,95), (52,117), (70,155), (115, 250), (155, 352). The evaluation of the proposed MD-TinyYOLOv4 model and the comparison results with the existing YOLO variants and SSD (Single Shot Detector) models are shown in Table 2.…”

Section: Proposed Md-tinyyolov4mentioning

confidence: 99%

“…Furthermore, the refined YOLOv4 [38] achieved an average accuracy of 67% and a speed of 38 fps in car, truck, and motorcycle tracking applications. Finally, although the YOLOv5 [39] model was used for safety vehicle detection mechanisms [40], reservations exist, as it was not developed by the original author of YOLO and is less innovative than previous versions [41]. Tiny architectures for the YOLO algorithm series were proposed in order to use fewer computational resources than the full-scale YOLO series, allowing for higher-speed performance, even on mobile devices or embedded systems [42].…”

Section: Introductionmentioning

confidence: 99%

Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle

Shabestari,

Hosseininaveh,

Remondino

2023

Remote Sensing

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Motorcycle detection and collision warning are essential features in advanced driver assistance systems (ADAS) to ensure road safety, especially in emergency situations. However, detecting motorcycles from videos captured from a car is challenging due to the varying shapes and appearances of motorcycles. In this paper, we propose an integrated and innovative remote sensing and artificial intelligence (AI) methodology for motorcycle detection and distance estimation based on visual data from a single camera installed in the back of a vehicle. Firstly, MD-TinyYOLOv4 is used for detecting motorcycles, refining the neural network through SPP (spatial pyramid pooling) feature extraction, Mish activation function, data augmentation techniques, and optimized anchor boxes for training. The proposed algorithm outperforms eight existing YOLO versions, achieving a precision of 81% at a speed of 240 fps. Secondly, a refined disparity map of each motorcycle’s bounding box is estimated by training a Monodepth2 with a bilateral filter for distance estimation. The proposed fusion model (motorcycle’s detection and distance from vehicle) is evaluated with depth stereo camera measurements, and the results show that 89% of warning scenes are correctly detected, with an alarm notification time of 0.022 s for each image. Outcomes indicate that the proposed integrated methodology provides an effective solution for ADAS, with promising results for real-world applications, and can be suitable for running on mobility services or embedded computing boards instead of the super expensive and powerful systems used in some high-tech unmanned vehicles.

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“…In 2020, after the release of YOLOv4, YOLOv5 was released. Although the original authors of YOLO did not publish papers on this version, it can be seen from other related papers, such as [32,33], that YOLOv5 is a masterpiece of previous versions, with excellent performance in detection accuracy and speed.…”

Section: Related Work 21 Object Detectionmentioning

confidence: 99%

Research on Object Detection and Recognition Method for UAV Aerial Images Based on Improved YOLOv5

Zhang

Shao

et al. 2023

Drones

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In this paper, an object detection and recognition method based on improved YOLOv5 is proposed for application on unmanned aerial vehicle (UAV) aerial images. Firstly, we improved the traditional Gabor function to obtain Gabor convolutional kernels with better edge enhancement properties. We used eight Gabor convolutional kernels to enhance the object edges from eight directions, and the enhanced image has obvious edge features, thus providing the best object area for subsequent deep feature extraction work. Secondly, we added a coordinate attention (CA) mechanism to the backbone of YOLOv5. The plug-and-play lightweight CA mechanism considers information of both the spatial location and channel of features and can accurately capture the long-range dependencies of positions. CA is like the eyes of YOLOv5, making it easier for the network to find the region of interest (ROI). Once again, we replaced the Path Aggregation Network (PANet) with a Bidirectional Feature Pyramid Network (BiFPN) at the neck of YOLOv5. BiFPN performs weighting operations on different input feature layers, which helps to balance the contribution of each layer. In addition, BiFPN adds horizontally connected feature branches across nodes on a bidirectional feature fusion structure to fuse more in-depth feature information. Finally, we trained the overall improved YOLOv5 model on our integrated dataset LSDUVD and compared it with other models on multiple datasets. The results show that our method has the best convergence effect and mAP value, which demonstrates that our method has unique advantages in processing detection tasks of UAV aerial images.

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A Safety Vehicle Detection Mechanism Based on YOLOv5

Cited by 11 publications

References 33 publications

Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition

Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition

Motorcycle Detection and Collision Warning Using Monocular Images from a Vehicle

Research on Object Detection and Recognition Method for UAV Aerial Images Based on Improved YOLOv5

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