如何在隔离用户会话中处理多视角 YOLO 检测对象的去重（尤其是 20x22px 小裁剪图）？
How to handle object deduplication across multiple camera angles using YOLO (especially for tiny 20x22px crops) across isolated user sessions? [D]
有开发者构建计算机视觉流水线，让用户上传同一场景的多角度照片，用 YOLO 检测目标并裁剪成轻量 .webp 图片，需保证每个物理对象在每个会话中只计一次，避免对已处理对象重复调用昂贵的下游 AI 描述。难点在于同一物体出现在 3 到 4 张不同角度照片中，且裁剪图仅约 20x22px。
I’m building a computer vision pipeline where users upload multiple photos of the same physical scene taken from different angles (front view, side view, close-up) to build a clean list of unique objects and their attributes.
I’m running YOLO for object detection to extract bounding boxes and crop each detected object into lightweight .webp images. Because the same physical object appears across 3 or 4 different photos from different angles, I need to make sure each unique object is only counted once per session, and avoid running expensive downstream AI calls to re-describe an object we already processed from an earlier photo.
submitted by /u/Expert-Ad598
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来源：r/MachineLearning · reddit.com