监控场景下基于CLIP的细粒度目标检测方法

CLIP-Based Fine-Grained Target Detection Method in Surveillance Scenarios

当前,随着国内摄像头数量的迅猛增长,每天所产生的庞大视频数据不仅对人力和物力资源构成了巨大的负担,而且导致了昂贵的成本开支。针对这一问题,本研究聚焦于解决细粒度目标检测领域存在的具体问题。本研究基于深度学习技术,结合Yolov4目标检测和CLIP特征分析,提出了一种综合的图像分析方法,以降低视频数据处理的成本。目前,现有的细粒度目标检测方法在处理大规模视频数据时面临着一系列挑战。这些挑战包括但不限于人工标注成本太高,而且无法保证标注的全面性,人工标注不如用户反馈及时有效;泛化能力只太弱,定制化成本太高,大多数AI任务都需要case by case实现。为了解决这些问题,本研究首先利用Yolov4模型对输入图像进行人物检测,以高效地实现目标的准确分割。随后,针对每个分割的人物,本实验采用CLIP模型进行深度特征分析,其泛化能力强且训练语料完全不需要人工标注的特点使捕捉图像和语言之间的语义精准关联。通过本研究的实验结果,本研究验证了该方法在人物检测方面的卓越表现,并展示了在基于CLIP的特征分析中显著的语义一致性。这一创新方法有望显著降低视频数据处理的成本和工作量,为细粒度目标检测领域的进一步研究提供了新的方向。

Currently, the proliferation of cameras in the nation has resulted in an immense volume of video data being produced on a daily basis, which is not only a huge strain on human and material re-sources, but also comes with a hefty price tag. This paper concentrates on resolving the particular difficulties associated with precise target recognition in order to address this issue. We propose an integrated image analysis method to reduce the cost of video data processing. This method is based on deep learning techniques, combined with Yolov4 target detection and CLIP feature analysis. Currently, there are a number of challenges that current target detection methods face when work-ing with large-scale video data. In addition to the expensive cost of manual tagging and the lack of assurance that it is comprehensive and that manual tagging is not as timely and effective as user feedback, generalization is only too weak, customization is too expensive, and most AI tasks need to be implemented on a case-by-case basis. To solve these problems, we first use the Yolov4 model to detect the characters of the input images in order to achieve accurate segmentation efficiently. The CLIP model is then used for indepth feature analysis for each segmented character. The ability to generalize and train language materials without manual tagging makes it possible to capture se-mantic and precise associations between images and languages. Our findings showcase the exceptional efficacy of this method in character detection and exhibit substantial semantic coherence in CLIP-based feature analysis. This novel approach is anticipated to drastically cut down on the ex-pense and labor of video data processing and open up fresh avenues for further exploration in the area of precise target recognition.