优化YOLOv4检测小目标，提升实时应用性能
Accelerating Object Detection with YOLOv4 for Real-Time Applications
- 改进YOLOv4结构，增强小目标检测能力
- 在复杂场景下实现高准确率与低延迟
- 适合嵌入式系统与实时监控场景
目标检测是计算机视觉的核心任务，广泛应用于安防监控、追踪系统等领域。随着实际应用需求增长，实时性要求日益严苛。尽管近年来卷积神经网络（CNN）在图像识别中表现优异，但传统目标检测模型在动态复杂环境中仍存在效率瓶颈。本文首先介绍深度学习与目标检测框架，特别是YOLOv4模型。随后提出一种改进的检测架构，通过结构优化显著提升对小目标的检测性能。实验表明，该方法在保持高精度的同时实现了更低的推理延迟，适用于资源受限的实时应用场景。
原文摘要 · Abstract (English)
Object Detection is related to Computer Vision. Object detection enables detecting instances of objects in images and videos. Due to its increased utilization in surveillance, tracking system used in security and many others applications have propelled researchers to continuously derive more efficient and competitive algorithms. However, problems emerges while implementing it in real-time because of their dynamic environment and complex algorithms used in object detection. In the last few years, Convolution Neural Network (CNN) have emerged as a powerful tool for recognizing image content and in computer vision approach for most problems. In this paper, We revived begins the brief introduction of deep learning and object detection framework like Convolutional Neural Network(CNN), You only look once - version 4 (YOLOv4). Then we focus on our proposed object detection architectures along with some modifications. The traditional model detects a small object in images. We have some modifications to the model. Our proposed method gives the correct result with accuracy.
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