Providing a dataset with a suitable volume and high accuracy for training deep neural networks is considered to be one of the basic requirements in that a suitable dataset in terms of the number and quality of images and labeling accuracy can have a great impact on the output accuracy of the trained network. The dataset presented in this article contains 3000 images downloaded from online Iranian car sales companies, including Divar and Bama sites, which are manually labeled in three classes: car, truck, and bus. The labels are in the form of 5765 bounding boxes, which characterize the vehicles in the image with high accuracy, ultimately resulting in a unique dataset that is made available for public use.The YOLOv8s algorithm, trained on this dataset, achieves an impressive final precision of 91.7% for validation images. The Mean Average Precision (mAP) at a 50% threshold is recorded at 92.6%. This precision is considered suitable for city vehicle detection networks. Notably, when comparing the YOLOv8s algorithm trained with this dataset to YOLOv8s trained with the COCO dataset, there is a remarkable 10% increase in mAP at 50% and an approximately 22% improvement in the mAP range of 50% to 95%.

Computer Vision is a field of study that helps to develop techniques to identify images and displays. It has various features like image recognition, object detection and image creation, etc. object detection is used for face detection, vehicle detection, web images, and safety systems. Its algorithms are Region-based Convolutional Neural Networks (RCNN), Faster-RCNN and You Only Look Once Method (YOLO) that have shown state-of-the-art performance. Of these, YOLO is better in speed compared to accuracy. It has efficient object detection without compromising on performance.

The main purpose of this paper is to develop the method of characteristic functions for calculating the detection characteristics in the case of the object surrounded by rough surfaces. This method is to be implemented in synthetic aperture radar (SAR) systems using optimal resolution algorithms. By applying the specified technique, the expressions have been obtained for the false alarm and correct detection probabilities. In order to illustrate the effective application of the introduced approach in the case of the generic SAR system, the results are presented of the calculation of the detection characteristics of the signal from the extended object surrounded by a rough surface. It is shown that the analysis allows us to substantiate the structure of the SAR signal processing channel and to obtain the improved relations for the radio observation characteristics in this case. The efficiency of the optimal signal processing in SAR systems can also be determined without the approximate calculations involved.

In this paper, a design and construction method for an omnidirectional vision system is described, including how to use it on autonomous soccer robots for object detection, localization and, also, collision avoidance in the middle size league of RoboCup. This vision system uses two mirrors, flat and hyperbolic. The flat mirror is used for detecting very close objects around the robot body and the hyperbolic one is used as a global viewing device to construct a world model for the soccer field. This world model contains information about the position and orientation of the robot itself and the position of other objects in a fixed coordinate system. In addition, a fast object detection method is introduced. It reduces the entire search space of an image into a small number of pixels, using a new idea that is called jump points. The objects are detected by examining the color of pixels overlapping these jump points and a few pixels in their neighborhood. Two fast and robust localization methods are introduced, using the angle of several fixed landmarks on the field and the perpendicular borderlines of the field. Borderline detection uses the clustering of candidate points and the Hough transform. In addition, the omnidirectional viewing system is combined with a front view that uses a plain CCD camera. This combination provided a total vision system solution that was tested in the RoboCup 2001 competitions in Seattle USA. Highly satisfactory results were obtained, both in object detection and localization in desired real-time speed.

object detection has been one of the areas of interest of research community for over years and has made significant advances in its journey so far. There is a tremendous scope in the applications that would benefit with more innovations in the domain of object detection. Rapid growth in the field of machine learning has complemented the efforts in this area and in the recent times, research community has contributed a lot in real time object detection. In the current work, authors have implemented real time object detection and have made efforts to improve the accuracy of the detection mechanism. In the current research, we have used ssd_v2_inception_coco model as Single Shot detection models deliver significantly better results. A dataset of more than 100 raw images is used for training and then xml files are generated using labellimg. Tensor flow records generated are passed through training pipelines using the proposed model. OpenCV captures real-time images and CNN performs convolution operations on images. The real time object detection delivers an accuracy of 92. 7%, which is an improvement over some of the existing models already proposed earlier. Model detects hundreds of objects simultaneously. In the proposed model, accuracy of object detection significantly improvises over existing methodologies in practice. There is a substantial dataset to evaluate the accuracy of proposed model. The model may be readily useful for object detection applications including parking lots, human identification, and inventory management.

This study addresses the challenges of change detection in Very High Resolution (VHR) satellite imagery, which are characterized by complex feature interactions, noise sensitivity, and intricate land use changes. We propose a novel approach that integrates object-based, feature-based, and dual learning-based methods to enhance the detection of changes on the Earth's surface using bi-temporal images. Our methodology begins with the separate segmentation of VHR images to manage complexities and account for pixel relationships. We then enrich spectral bands with additional textural, mathematical, and geometrical features derived from paired images to conduct a comprehensive conceptual analysis. Finally, we implement two categories of supervised classifiers—individual and ensemble—to generate a binary change map that classifies regions as either changed or unchanged. The results from experiments on two VHR datasets demonstrate that our approach outperforms traditional methods, achieving the highest F1-score and Intersection over Union (IoU) of 99.02% and 96.78%, respectively. The novelty of this research lies in the integration of object-based, feature-based, and learning-based approaches, leading to a comprehensive feature extraction process. Additionally, our method creates detailed change detection maps with numerous homogeneous areas, contributing significantly to advancements in the field.