# An Image-Retrieval Method Based on Cross-Hardware Platform Features
It organizes video content into structured information that can be understood by computers and people and semi-structured information that can be compared according to semantic relationship by means of spatiotemporal segmentation, feature extraction, object recognition. Video structuring can capture and extract structured information of motor vehicles, license plates, non-motor vehicles, and other information in video-streaming media in real time, aiming at efficient extraction and structured storage of massive video information within a certain spatiotemporal range, greatly reducing the spatiotemporal range of retrieval, achieving structured data research and judgment of specific targets or large amounts of data, and improving storage and retrieval efficiency. It has high practical value for target trajectory research, target quick search, traffic-situation analysis, privacy protection, and other directions. Feature extraction in image-retrieval technology requires a lot of computing resources, so engineers use multi-chip hybrid deployment to extract features. However, due to differences in AI inference chips, it is difficult to ensure consistency of accuracy when deploying AI models based on heterogeneous hardware, especially for feature-based image retrieval, which is sensitive to accuracy [23; 24; 25; 26]. Therefore, it is particularly important to verify the accuracy consistency of heterogeneous hardware and reduce the loss of accuracy across platforms. After verifying the feature consistency of heterogeneous hardware, the features of different hardware platforms can be directly used in production without considering the differences between hardware, which further expands the deployment range of algorithm schemes and has significant practical value. In feature extraction, the main factors that affect feature quality include codec, color-space conversion, image scaling, model inference (target bounding box, model quantization, algorithm scheme model version, image matting, operator fusion), etc. In this paper, the AI model uses FP16 quantization, which can strike a good balance between precision and speed [8; 17]. In this paper, we focus on the implementation of an image-retrieval method based on cross-hardware platform features, which reduces the cross-platform accuracy loss, and proves that the features of heterogeneous hardware platforms can be mixed, in which the Huawei Atlas 300V and NVIDIA TeslaT4 are used for experiments. First, we compare the decoding differences of heterogeneous hardware, and use CPU software decoding to help hardware decoding improve the decoding success rate. Also, the color-space conversion module is added to further improve the image utilization rate. Then, we compared the difference between the Atlas 300V and TeslaT4 chip architectures and tested the differences between the two platform features by calculating feature similarity. In addition, the scaling mode in the pre-processing process is also compared to further analyze the factors affecting the feature consistency. Next, the consistency of capture and correlation based on video structure is verified. Finally, the experimental results reveal that the feature results from the TeslaT4 and Atlas 300V can be mixed for image retrieval based on cross-hardware platform features. Consequently, cross-platform image retrieval with low error is realized.