Real-time Vehicle Detection and Distance Estimation: Soft-sensor Approach Using Optimized You Only Look Once Version 5 and Perspective Geometry

With the rapid development of Internet of Things (IoT) and UAV technology, for the whole IoT system of vehicle detection, the middle and high level of information transmission and server processing has made a breakthrough, so at the bottom of the real-time detection of the vehicle by the UAV is the key to the whole system. However, UAV vehicle detection faces the challenges of too many small targets in the image leading to low detection accuracy, limited hardware platform resources requiring control of model size, and real-time detection requiring high inference speed. Aiming at the above problems, we propose a lightweight model PrFu-YOLO based on YOLOv8 improvement, which achieves a good balance between the accuracy, inference speed, and model size. And it realizes real-time vehicle detection embedded in an UAV platform. To solve the problem of low vehicle detection accuracy, we design a new structure PrFuFPN based on adding a small target detection layer to achieve more advanced feature fusion. To address the limited resources of the platform and the problem of real-time vehicle detection, we add GhostConv to the structure and constantly try to adjust the parameters of the network. Finally, extensive experiments were conducted on the VisDrone2019 and CARPK data sets to fully evaluate the model. Compared to YOLOv8s on the VisDrone2019 test set, mAP50 was improved by 10.05%, mA95 by 14.54%, the number of parameters was reduced by 8.13%, and the model size was reduced by 12.5%, while the FPS of 67.13 fully met the needs of real-time detection.

Vehicle detection is an important component in unmanned driving systems. This paper presents a real-time vehicle detection method under complex road conditions to solve the problem of real-time detection of road vehicles in automatic driving. Firstly using yolo network to build a deeper depth neural network, which is used to identify and score the vehicles in the image. Then, using the dynamic threshold method to remove some false candidate boxes, and using the Gauss attenuation function to filter the overlapping candidate boxes. The loss function normalized by the change rate is used to train the neural network, and the batch normalization layer is used to correct the input data of the network to avoid data deviation during the training process. Finally, a full convolution layer is added at the end of the network layer to transform the two-dimensional data into one-dimensional data and output the final recognition results. It is verified that this method improves the efficiency and accuracy of real-time vehicle detection, it can effectively detect vehicles on roads with complex backgrounds, and satisfy the real-time requirements of road vehicle detection.

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration.

Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification

The precise vehicle detection, following, and speed estimation are the essential elements of intelligent transportation system and traffic analytics. Conventional systems tend to be based on manual calibration or multi-sensors, which restrict scalability and flexibility. The paper introduces a vision-based, fully automated real-time vehicle monitoring system that combines the YOLO v11 object detector, an Enhanced SORT tracker to provide very robust multi-object tracking and an automatic camera calibration system to self-estimate the pixel-to-meter conversion ratio. The suggested system does not require manual calibration and takes advantage of such dimensions as typical vehicles, confidence-weighted fusion, and statistical filtering to stabilize scale estimation. Enhanced SORT tracker uses a Kalman filter-based motion model, and IOU + LAP association to get the same object ID and extract the velocity. Experimental results of testing on several traffic video streams show a mean processing rate of 25-30 frames/s, error of speed estimation of +-2 km/h and convergent calibration within 45 seconds. Structured CSV and JSON analytics are also produced by the system to analysis additional traffic flow and anomalies. The findings are that the suggested solution is a cost-efficient, scalable, and precise solution to real-time traffic monitoring and vehicle behavior analytics.

The Vehicle detection is important for the enhancement of transportation systems and which is efficient for traffic management, improved road safety and accurate data collection by automatically identifying and tracking vehicles on roads which enables features like traffic signal optimization, speed measurement and accident detection ultimately contributing to a smoother and safer driving experience for everyone. Here we have built a real-time project which can detect car, bus, motorcycle and truck on the basis of algorithm called YOLOV8 (You Only Look Once Version 8). It is a computer vision technique which is renowned for its real-time object detection capabilities, providing optimal balance between speed and accuracy. The model is trained using PyTorch and leverages Convolutional Neural Network (CNN) for feature extraction.

Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.

Vehicle detection from short-range aerial image faces challenges including vehicle blocking, irrelevant object interference, motion blurring, color variation etc., leading to the difficulty to achieve high detection accuracy and real-time detection speed. In this paper, benefiting from the recent development in MobileNet family network engineering, we propose a compressed MobileNet which is not only internally resistant to the above listed challenges but also gains the best detection accuracy/speed tradeoff when comparing with the original MobileNet. In a nutshell, we reduce the bottleneck architecture number during the feature map downsampling stage but add more bottlenecks during the feature map plateau stage, neither extra FLOPs nor parameters are thus involved but reduced inference time and better accuracy are expected. We conduct experiment on our collected 5-k short-range aerial images, containing six vehicle categories: truck, car, bus, bicycle, motorcycle, crowded bicycles and crowded motorcycles. Our proposed compressed MobileNet achieves 110 FPS (GPU), 31 FPS (CPU) and 15 FPS (mobile phone), 1.2 times faster and 2% more accurate (mAP) than the original MobileNet.

Increasing driving safety by virtue of advanced technology requires real-time and accurate detection of vehicles in far and close distance, which pose a threat to the host vehicle. This paper presents a real-time on-road vehicle detection algorithm based on monocular vision. First, auto-adapted threshold segmentation is proposed to extract shadow features. Then, a special mask is used for morphology computing to retain the features of vehicles in far and close distance. And, vanishing point constraint is applied for the fast verification of vehicles. Finally, the tracking of vehicles assists to stabilize the detection results. The experiments show that the average processing speed reaches 39 frames/ms, and the fast detection of vehicle under different weather conditions in the day time can also work accurately.

This paper presents a lightweight and efficient vehicle detection and tracking method for smart intersection environments using a low-cost, low-resolutionFMCW(Frequency Modulated Continuous Wave) LiDAR(Light Detecting And Ranging) sensor.We propose a framework that leverages Doppler-based velocity information fromFMCWLiDAR to facilitate point cloud accumulation. This leads to more effective background removal and robust multi-object tracking, even with low-resolution LiDAR data. The point cloud accumulation technique compensates for the sparsity of points representing vehicles by leveraging the radial velocity of detected points, resulting in denser and more accurate representations of vehicles. Background removal and lane segmentation are applied to effectively remove static objects and improve detection accuracy. The Kalman filter incorporates both position and velocity information of detected objects, enabling reliable tracking even when frame rates are low or objects move rapidly. The overall system is designed for real-time operation on edge devices, making it suitable for deployment in practical smart city scenarios. Experimental validation using real-world road data demonstrates that the proposed method significantly enhances detection and tracking performance compared to conventional approaches.

Traditional deep learning-based vehicle detection methods are often designed using a pyramid of filters with multiple scales and sizes; therefore, the processing time is slow due to the large number of scales used and because the classifier runs at all scales. Recently, a deep learning-based region proposal network was introduced to detect vehicles that only employ the network one time regardless of the size of the input image. In object detection, deep learning-based region proposal networks have achieved state-of-the-art performance in terms of accuracy. These systems achieve a very high accuracy under normal driving conditions; however, their performance decreases under difficult driving conditions such as in snow, rain, or fog. In addition, the current state-of-the-art system-based region proposal networks still fail to satisfy the real-time requirements of the driving assistant systems. More recently, the identification of local patterns has been shown to improve the performance of the traditional deep-learning systems; hence, this paper investigates local patterns in region proposal networks to improve their accuracy. Depth information is also investigated to improve the processing time of current region proposal networks. Our experimental results show that the proposed system obtains better performance than the state-of-the-art object region detection systems in terms of both accuracy and processing time.

This paper aims at real-time vehicle detection and tracking. It presents a novel comprehensive algorithm by multi-cues fusion in sequence images based on monocular vision. Firstly, the vertical symmetry of vehicle rear view is utilised to extract the Region of Interest (ROI) so as to narrow the search area. And then, the sign of underneath shadow is employed to generate hypothetical positions on which potential vehicles maybe present. Following, both image intensity and figure information are used to verify the vertical symmetry of the potential vehicle candidates. Meanwhile, Mean Shift, based on the object features’ model of combine colour Histogram and Orientation Histogram (HOG), is employed to fast search the potential objects. More important, both detection and tracking are under an interactive mechanism which can dramatically improve detection efficiency. Experimental results show this approach can achieve 96.34% accurate rate and run on an average

This paper introduces YOLOv11-SEG, a real-time vehicle detection with instance segmentation using deep learning for Advanced Driver Assistance Systems (ADAS) and autonomous driving. The C3k2 modules for more accurate feature extraction and C2PSA attention for the treatment of small or occluded objects are added to the YOLO architecture. Trained with a fiveclass vehicle dataset of 6,500 instances, the model achieves box detection precision of 94.5% and segmentation precision of 94.9%, with recall of 94.4% (box) and 93.3% (mask) showing its high sensitivity. mAP@0.5 values of 97.4% (detection) and 97.1% (segmentation) show accurate object localization, while mAP@0.5-0.95 values of 90.4% and 88.5% show robust performance at varying IoU thresholds. Qualitative tests demonstrate consistency in performance in unfavorable conditions of fog, low light, and occlusion, showing the suitability of YOLOv11-SEG for real-world deployment in ADAS. Future work will be focused on accelerating the speed of inference and reducing the computational complexity, enhancing the applicability of the model for safe, efficient autonomous driving.

With the increase in the number of vehicles in our country, traffic accidents have become frequent. The real-time detection of dense traffic vehicles is particularly important, which can promote the development of autonomous driving and lay a good foundation for intelligent transportation. Aiming at the problems such as high vehicle overlap rate, high small target missing rate and poor real-time detection in traffic vehicle data set, a traffic vehicle target detection method based on improved YOLOv7 was proposed. Firstly, the neck network structure of the original YOLOv7 is lightweight and introduced into the GhostSlimPAN paradigm structure to reduce the number of model parameters and operation cost, and improve the accuracy of detection of small target vehicles. Then, the original loss function CIoU is improved to replusion loss function to reduce the missed and false detection rate caused by mutual occlusion between vehicles. In order to verify the effect of the improved model, the vehicle dataset was selected for testing and verification. The experimental results show that compared with the original YOLOv7 algorithm, the [email protected] of the improved YOLOv7 algorithm on this dataset reaches 88.2%, which is 1.8% higher than that of the benchmark network. The improved algorithm can be applied to vehicle target detection in daily environment, providing key technical support for intelligent transportation and autonomous driving.

In recent years, deep learning models have seen extensive use in various domains, with the YOLO algorithm family emerging as a prominent player.YOLOv5, known for its real-time object detection capabilities and high accuracy, has been widely embraced in transportationrelated research.However, the introduction of YOLOv8 in early 2023 signifies a significant leap forward in object detection technology.Despite its potential, the literature on YOLOv8 remains relatively scarce, leaving room for exploration and adoption in research.This study pioneers real-time vehicle detection using the YOLOv8 algorithm.An in-depth analysis of YOLOv8n, the smallest scale model within the YOLOv8 series, was conducted to assess its suitability for real-time scenarios, particularly in Intelligent Transportation Systems (ITS).To reinforce its real-time capabilities, a parametric analysis covering image processing time, detection sensitivity, and input image characteristics was performed.To optimize model performance, a training dataset was created through flight tests using a custom autonomous drone, encompassing various vehicle variations.This ensures that the model excels in recognizing diverse motor vehicle configurations.The results reveal that even this compact sub-model achieves an impressive detection accuracy rate exceeding 80%.The study establishes that YOLOv8n, evaluated for the first time in ITS applications, effectively serves as an object detector for real-time smart traffic management.