To balance vehicle detection accuracy and speed, we proposed a lightweight vehicle detection model, LVD-YOLO, based on YOLOv5s, suitable for resource-constrained environments. Thanks to our use of EfficientNetV2
In order to address the detection of vehicles and pedestrian targets of self-driving vehicles during their driving on the road, this paper puts forward an improved YOLO model based on Multiscale Retinex with Colour Restoration defogging algorithm. In the proposed approach, different YOLO models (...
“Pretrained YOLO v2 For Object Detection,” https://github.com/matlab-deep-learning/Object-Detection-Using-Pretrained-YOLO-v2. Detect objects using YOLO v4 object detector - MATLAB. (n.d.). www.mathworks.com. https://www.mathworks.com/help/vision/ref/yolov4objectdetector.html Lee, C.C....
To address the above issues, we optimized the base YOLOv8 model. To avoid inefficiency in the improved network and considering the computational limitations for future deployment on embedded platforms, we made modifications based on the YOLOv8n version. We optimized both the backbone and detection ...
[33] use YOLOv2 for detecting vehicles in aerial imagery and also explore a new data annotation method. The YOLOv2 uses a single CONV network to predict the bboxes and the class probabilities simultaneously. Since YOLOv2 uses contextual information about the objects’ appearances, it has ...
To ensure the dataset is suitable for training and testing detectors, two different bounding box formats have been created: pascal_voc, (which supports all deep neural network models except YOLO), so separate labeling is done for YOLO models and updated in the dataset. To train the latest ...
OD-YOLOv8: A Lightweight and Enhanced New Algorithm for Ship Detection Computer Systems Science and Engineering, Vol.49, pp. 377-399, 2025, DOI:10.32604/csse.2025.059634- 09 April 2025 AbstractSynthetic Aperture Radar (SAR) has become one of the most effective tools in ship detection. However...
Since positive and negative images consist of vehicles, this approach is not suitable for open parking lot. Deep learning has improved the detection process and a wide variety of architectures were introduced. Few such architectures which are fast and computationally efficient are Yolo, GoogLeNet, ...
The anchor-free object detection CenterNet has the problems that the utilization rate of detected object features is low, which is difficult to detect morp
Therefore, to enhance the perception and environment understanding of vehicles in complex urban environments, an improved version of Cooperative, Connected and Automated Mobility (CCAM) is needed, which is expected to push forward automated driving to the next level of vehicle automation such as L-4...