HIGH-PRECISION AERIAL OBJECT DETECTION MODEL UTILIZING YOLO V10 DEEP NEURAL NETWORK

Enhanced Object tracking in videos: integrating CEDSegNet with adjusted covariance-based mean shift algorithm

A traffic flow statistical detection method based on computer vision technology

This article addresses the issues of high deployment costs, low accuracy in small object detection, and easy ID switching in multi-target tracking of existing traffic flow statistics methods. Based on the visual sensing mechanism of visible light imaging, this article designs and implements a traffic flow statistics system based on YOLOv8 object detection algorithm and ByteTrack multi-target tracking algorithm. The system first constructed a multi scene traffic dataset containing approximately 9000 images; Then, the YOLOv8n model is used for training, and the model's generalization ability is improved through hyperparameter tuning and data augmentation strategies; Subsequently, the TensorRT inference framework was used to perform FP16 semi precision acceleration on the model, increasing the inference frame rate from 54.62 FPS to 63.37 FPS, with an increase of 16.01%; Finally, the ByteTrack algorithm is integrated to achieve multi-target tracking, and a virtual line based cross line counting method is designed to achieve bidirectional traffic flow statistics. The experimental results show that the counting accuracy of the system reaches 96%, 97%, and 94% respectively in three typical scenarios: daytime intersections, nighttime highways, and daytime congestion. It has good practical value and prospects for promotion and application, and also provides a technical basis for the subsequent introduction of infrared thermal imaging multimodal fusion to enhance low illumination robustness.

AS-YOLOR: An Improved YOLO Model for Small Object Detection in Aerial Images

An Adaptive and Scalable YOLO model named AS-YOLOR (Adaptive and Scalable YOLO for Rotated object detection), based on the YOLOv8 baseline is proposed, providing a solution with strong practical potential for achieving efficient and high-precision detection of small, rotated objects.

Detection and tracking of small-sized vehicles in aerial video from UAVs

Vehicle detection and tracking in unmanned aerial vehicle (UAV) imagery, while critical for intelligent transportation systems, remain challenging due to high omission rates, false alarms, and frequent identity switches among small-sized vehicles. The proposed research establishes an enhanced tracking-by-detection framework integrating an improved YOLOX with DeepSORT to mitigate the aforementioned challenges. Integrating the Convolutional Block Attention Module (CBAM) into the feature fusion stage optimizes feature extraction within complex environments. Furthermore, the detection head is refined by replacing standard Binary Cross-Entropy (BCE) and IoU losses with Varifocal Loss and Enhanced IoU (EIoU) loss, respectively, to mitigate sample imbalance and boost localization accuracy. A sliding-window-based image slicing method is also introduced to enhance detection sensitivity to small-sized spatial features. Experimental results demonstrate that the proposed method significantly reduces detection errors and identity switches while strengthening tracking stability.

Object Detection and Tracking, Searching Using Computer Vision and YOLO Technique

Real-time visual understanding has become a cornerstone of modern intelligent systems, spanning surveillance, autonomous navigation, retail analytics, and smart-city infrastructure. The system architecture is described in detail, including data-flow diagrams, use-case and sequence diagrams, an activity diagram, and a relational database design for persisting detection and tracking metadata. An experimental evaluation performed on standard benchmark-style data demonstrates that the proposed pipeline attains a mean Average Precision (mAP@0.5) of approximately 0.91, a tracking identity-switch rate reduced by 38% relative to a naive frame-by-frame detector, and a sustained throughput of 61 frames per second on a mid-range GPU, outperforming several baseline architectures compared in this study. The results confirm that combining YOLOv8 with Deep SORT and a dedicated search layer yields a practical, extensible platform for real-time object detection, tracking, and retrieval applications.

A Review of Deep Learning Object Detection Algorithms

Object detection and multi-object tracking are core foundational tasks in computer vision, serving as crucial supporting technologies for high-end applications such as autonomous driving, intelligent security, industrial inspection, and robot navigation. This paper systematically reviews the complete evolution of object detection and multi-object tracking technologies from traditional manual feature extraction methods to the era of deep learning, focusing on the core principles, technological advantages, and application limitations of single-stage and two-stage detectors. Building upon this foundation, it further summarizes the three widely accepted mainstream paradigms in multi-object tracking — separate detection and feature extraction, joint detection and feature extraction, and joint detection and tracking — and discusses in depth the key technologies under each paradigm, including motion feature modeling, deep appearance feature learning, attention mechanism optimization, and the fusion application of the Transformer architecture. Finally, it summarizes the core challenges currently facing the field (balancing real-time performance and accuracy, small object detection, occlusion robustness, etc.) and looks forward to key future development directions, providing a systematic reference for theoretical research and engineering implementation in related fields.