实例分割是图像分割的重要组成部分,同时也是计算机视觉中的一个关键研究课题,广泛应用于自动驾驶和安全监控等领域。然而,由于道路场景通常具有复杂性、多样性和杂乱的特点,处理这些场景变得尤为挑战。针对道路场景图像实例分割难度大、精度低、定位不精确等问题,本文提出一种基于改进YOLOv5(You Only Look Once version 5)的道路场景实例分割算法。以YOLOv5为基础模型,在Head模块中采用RFAConv(Receptive-Field Attention Convolution)卷积代替部分传统卷积,它全面解决了卷积核的参数共享问题,考虑到接受域中每个特征的重要性,提供了几乎可以忽略不计的计算成本和参数增量,能够更好地捕捉和融合图像特征,提升分割的精度和鲁棒性。采用ShapeIOU代替YOLOv5中原损失函数CIOU(Complete-IoU),通过聚焦边框自身形状与自身尺度计算损失,使得边框回归更为精确,能够有效提升检测效果且优于现有方法。实验结果表明:与原模型相比,改进后的模型的分割精度mAP50(mean Average Precision)达到了33.8%,相较于YOLOv5s,优化后的模型在分割精度上提高了1.2%,能够更加高效地完成道路场景的图像分割任务。
随着深度学习技术的迅速发展,关键点检测技术在医学影像分析中的应用受到广泛关注,尤其在超声、CT和MRI等医学影像中表现出巨大的潜力。文章首先回顾了传统的关键点检测技术与基于深度学习的关键点检测技术在医学影像中的应用,重点分析了卷积神经网络(CNN)、Hourglass网络和Transformer模型的特点与优势;随后讨论了关键点检测在医学影像中的实际应用,包括人体姿势估计、器官与肿瘤的分割与定位等领域的应用。此外,文章还总结了当前技术面临的挑战,如数据不足、图像噪声、跨设备泛化等问题,并提出了可能的解决方案。最后,结合深度学习的最新进展,本文展望了医学影像中关键点检测技术的未来发展趋势,旨在为医学影像分析中的关键点检测技术的研究与应用提供理论支持和发展思路。With the rapid development of deep learning technology, the application of keypoint detection technology in medical image analysis has received widespread attention, especially in medical images such as ultrasound, CT, and MRI, showing great potential. The article first reviews the application of traditional keypoint detection techniques and deep learning based keypoint detection techniques in medical imaging, with a focus on analyzing the characteristics and advantages of convolutional neural networks (CNN), Hourglass networks, and Transformer models;Subsequently, the practical applications of keypoint detection in medical imaging were discussed, including human pose estimation, segmentation and localization of organs and tumors, and other fields. In addition, the article also summarizes the challenges currently faced by technology, such as severe data shortages, image noise, cross device generalization, and proposes possible solutions. Finally, based on the latest advances in deep learning, this article looks forward to the future development trends of keypoint detection technology in medical imaging, aiming to provide theoretical support and development ideas for the research and application of keypoint detection technology in medical image analysis.
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