{"id":44306,"date":"2025-06-18T09:34:14","date_gmt":"2025-06-18T01:34:14","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/44306.html"},"modified":"2025-06-18T09:34:14","modified_gmt":"2025-06-18T01:34:14","slug":"%e7%9b%ae%e6%a0%87%e6%a3%80%e6%b5%8b-yolov12%e7%ae%97%e6%b3%95%e8%a7%a3%e8%af%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/44306.html","title":{"rendered":"\u76ee\u6807\u68c0\u6d4b\u2014\u2014YOLOv12\u7b97\u6cd5\u89e3\u8bfb"},"content":{"rendered":"<p>\u8bba\u6587&#xff1a;YOLOv12: Attention-Centric Real-Time Object Detectors (2025.2.18) \u4f5c\u8005&#xff1a;Yunjie Tian, Qixiang Ye, David Doermann \u94fe\u63a5&#xff1a;https:\/\/arxiv.org\/abs\/2502.12524 \u4ee3\u7801&#xff1a;https:\/\/github.com\/sunsmarterjie\/yolov12<\/p>\n<hr \/>\n<p>YOLO\u7cfb\u5217\u7b97\u6cd5\u89e3\u8bfb&#xff1a; YOLOv1\u901a\u4fd7\u6613\u61c2\u7248\u89e3\u8bfb\u3001SSD\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv2\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv3\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv4\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv5\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOR\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOX\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv6\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv7\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv8\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv9\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv10\u7b97\u6cd5\u89e3\u8bfb\u3001YOLO11\u7b97\u6cd5\u89e3\u8bfb\u3001YOLOv12\u7b97\u6cd5\u89e3\u8bfb<\/p>\n<p>PP-YOLO\u7cfb\u5217\u7b97\u6cd5\u89e3\u8bfb&#xff1a; PP-YOLO\u7b97\u6cd5\u89e3\u8bfb\u3001PP-YOLOv2\u7b97\u6cd5\u89e3\u8bfb\u3001PP-PicoDet\u7b97\u6cd5\u89e3\u8bfb\u3001PP-YOLOE\u7b97\u6cd5\u89e3\u8bfb\u3001PP-YOLOE-R\u7b97\u6cd5\u89e3\u8bfb<\/p>\n<p>R-CNN\u7cfb\u5217\u7b97\u6cd5\u89e3\u8bfb&#xff1a; R-CNN\u7b97\u6cd5\u89e3\u8bfb\u3001SPPNet\u7b97\u6cd5\u89e3\u8bfb\u3001Fast R-CNN\u7b97\u6cd5\u89e3\u8bfb\u3001Faster R-CNN\u7b97\u6cd5\u89e3\u8bfb\u3001Mask R-CNN\u7b97\u6cd5\u89e3\u8bfb\u3001Cascade R-CNN\u7b97\u6cd5\u89e3\u8bfb\u3001Libra R-CNN\u7b97\u6cd5\u89e3\u8bfb<\/p>\n<hr \/>\n<h4>\u6587\u7ae0\u76ee\u5f55<\/h4>\n<ul>\n<li>1\u3001\u7b97\u6cd5\u6982\u8ff0<\/li>\n<li>2\u3001YOLOv12\u7b97\u6cd5\u7ec6\u8282<\/li>\n<li>\n<ul>\n<li>2.1 \u533a\u57df\u6ce8\u610f\u529b\u673a\u5236Area Attention<\/li>\n<li>2.2 \u6b8b\u5dee\u9ad8\u6548\u805a\u5408\u7f51\u7edcResidual Efficient Layer Aggregation Networks<\/li>\n<li>2.3 \u6574\u4e2aYOLO\u7f51\u7edc\u7ed3\u6784\u4f18\u5316<\/li>\n<\/ul>\n<\/li>\n<li>3\u3001\u4ee3\u7801\u89e3\u6790<\/li>\n<li>4\u3001\u5b9e\u9a8c<\/li>\n<\/ul>\n<hr \/>\n<h2>1\u3001\u7b97\u6cd5\u6982\u8ff0<\/h2>\n<p>\u867d\u7136\u73b0\u5982\u4eca\u81ea\u6ce8\u610f\u673a\u5236\u5728\u5efa\u6a21\u80fd\u529b\u65b9\u9762\u5177\u6709\u4f18\u52bf&#xff0c;\u4f46\u5728\u901f\u5ea6\u65b9\u9762\u4ecd\u7136\u4e0d\u80fd\u548c\u4f20\u7edfCNN\u7ed3\u6784\u76f8\u6bd4&#xff0c;\u6240\u4ee5\u4ee5\u5f80\u7684YOLO\u7ed3\u6784\u90fd\u4e13\u6ce8\u4e8e\u5229\u7528CNN\u8fdb\u884c\u6539\u8fdb&#xff0c;\u800cYOLOv12\u7684\u4f5c\u8005\u63d0\u51fa\u5229\u7528\u81ea\u6ce8\u610f\u529b\u673a\u5236\u5bf9YOLO\u7ed3\u6784\u8fdb\u884c\u4f18\u5316\u3002\u76f8\u6bd4\u4e8e\u4e4b\u524dYOLO\u7cfb\u5217\u4ee5\u53caDETR\u7cfb\u5217&#xff0c;\u5728\u63a8\u7406\u901f\u5ea6\u3001COCO\u6570\u636e\u96c6\u4e0amAP\u53caFlops\u6570\u91cf\u4e0a\u90fd\u5177\u6709\u4f18\u52bf\u3002\u5982\u4e0b\u56fe\u6240\u793a&#xff1a; <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/06\/20250618013410-68521792c6ec8.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> YOLOv12\u4e3b\u8981\u6539\u8fdb\u6709\u5982\u4e0b\u51e0\u70b9&#xff1a; 1\u3001 \u63d0\u51fa\u4e86\u533a\u57df\u6ce8\u610f\u529b\u6a21\u5757(area attention module, A2),\u5728\u4fdd\u6301\u8f83\u5927\u611f\u53d7\u91ce\u7684\u524d\u63d0\u4e0b\u51cf\u5c11\u4e86\u8ba1\u7b97\u590d\u6742\u5ea6\u3002 2\u3001 \u5f15\u5165\u6b8b\u5dee\u9ad8\u6548\u805a\u5408\u6a21\u5757(residual efficient layer aggregation networks, R-ELAN)\u89e3\u51b3\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u6ce8\u610f\u529b\u4f18\u5316\u95ee\u9898\u3002\u5b83\u662f\u57fa\u4e8eYOLOv7\u4e2d\u7684ELAN\u6a21\u5757\u8fdb\u884c\u4e86\u4e24\u70b9\u6539\u8fdb&#xff1a;(1)\u3001block\u7ea7\u522b\u7684\u7f29\u653e\u52a0\u6b8b\u5dee\u8fde\u63a5&#xff1b;(2)\u3001\u91cd\u65b0\u8bbe\u8ba1\u7684\u805a\u5408\u65b9\u5f0f\u3002 3\u3001 \u57fa\u4e8e\u6574\u4e2aYOLO\u67b6\u6784\u4e5f\u505a\u4e86\u4e9b\u4f18\u5316\u6539\u8fdb&#xff0c;\u5305\u62ec&#xff1a;\u5f15\u5165flashattention\u3001\u53bb\u9664\u4f4d\u7f6e\u7f16\u7801\u5f15\u5165\u53ef\u5206\u79bb\u5377\u79ef\u3001\u8c03\u6574MLP\u6bd4\u7387\u5e76\u7528\u5377\u79ef\u66ff\u6362\u5168\u8fde\u63a5\u3001\u51cf\u5c11\u5806\u53e0\u5757\u7684\u6570\u91cf\u3002<\/p>\n<hr \/>\n<h2>2\u3001YOLOv12\u7b97\u6cd5\u7ec6\u8282<\/h2>\n<p>YOLO12\u7f51\u7edc\u7ed3\u6784\u5982\u4e0b\u6240\u793a <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/06\/20250618013410-68521792f0f76.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>YOLOv12\u662f\u57fa\u4e8eultralytics\u5f00\u53d1\u7684&#xff0c;\u5bf9\u6bd4YOLO11\u7684yaml\u914d\u7f6e\u6587\u4ef6&#xff0c;\u53ef\u4ee5\u770b\u51fa\u4e24\u8005\u7684\u4e0d\u540c\u4e4b\u5904\u3002\u5de6\u8fb9\u4e3aYOLO11&#xff0c;\u53f3\u8fb9\u4e3aYOLOv12\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/06\/20250618013411-6852179336fd2.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u53ef\u4ee5\u770b\u5230&#xff0c;\u5728backbone\u4e2d&#xff0c;YOLOv12\u5c06YOLO11\u4e2d\u6700\u540e\u4e24\u4e2aC3k2\u66ff\u6362\u6210\u4e86\u65b0\u63d0\u51fa\u7684A2C2f&#xff0c;\u5e76\u4e14\u53bb\u6389\u4e86SPPF\u548cC2PSA\u6a21\u5757\u3002\u800c\u5728head\u4e2d&#xff0c;\u9664\u4e86\u6700\u540e\u4e00\u4e2aC3k2&#xff0c;\u4e5f\u662f\u5c06YOLO11\u4e2d\u5176\u4f59\u7684C3k2\u66ff\u6362\u6210\u4e86A2C2f\u3002<\/p>\n<h3>2.1 \u533a\u57df\u6ce8\u610f\u529b\u673a\u5236Area Attention<\/h3>\n<p>\u56fe\u50cf\u81ea\u6ce8\u610f\u529b(self-attention)\u5929\u751f\u5c31\u6bd4CNN\u6162&#xff0c;\u8fd9\u7531\u4e0b\u9762\u4e24\u4e2a\u56e0\u7d20\u9020\u6210\u7684&#xff0c;\u590d\u6742\u5ea6\u548c\u8ba1\u7b97\u65b9\u5f0f\u3002 \u81ea\u6ce8\u610f\u673a\u5236\u8fd0\u7b97\u7684\u8ba1\u7b97\u590d\u6742\u5ea6\u4e0e\u8f93\u5165\u5e8f\u5217\u957f\u5ea6L\u6210\u4e8c\u6b21\u5173\u7cfb&#xff0c;\u6240\u4ee5\u5bf9\u4e8e\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u6216\u8005\u957f\u5e8f\u5217\u6765\u8bb2&#xff0c;\u590d\u6742\u5ea6\u4f1a\u6210\u500d\u589e\u52a0&#xff0c;\u518d\u52a0\u4e0a\u56fe\u50cf\u5207\u5757\u64cd\u4f5c\u548c\u4f4d\u7f6e\u7f16\u7801\u7b49\u989d\u5916\u64cd\u4f5c\u4e5f\u4f1a\u589e\u52a0\u6574\u4f53\u8017\u65f6\u3002\u81ea\u6ce8\u610f\u529b\u673a\u5236\u5728\u8ba1\u7b97\u8fc7\u7a0b\u4e2d\u4f1a\u9700\u8981\u6bd4CNN\u8ba1\u7b97\u66f4\u5927\u7684\u663e\u5b58\u6765\u7f13\u5b58\u6ce8\u610f\u529b\u56fe(QK\u77e9\u9635)\u548csoftmax\u56fe(LxL)\u3002\u7531\u4e8e\u4e8c\u6b21\u65b9\u8ba1\u7b97\u590d\u6742\u6027\u548c\u4f4e\u6548\u7684\u5185\u5b58\u8bbf\u95ee\u8fd9\u4e24\u4e2a\u56e0\u7d20&#xff0c;\u5171\u540c\u5bfc\u81f4\u81ea\u6ce8\u610f\u529b\u673a\u5236\u6bd4CNN\u6162\u3002\u6240\u4ee5\u5f88\u591a\u7814\u7a76\u8005\u63d0\u51fa\u4e86\u5bf9\u81ea\u6ce8\u610f\u529b\u673a\u5236\u8ba1\u7b97\u7684\u4f18\u5316&#xff0c;\u5982\u4e0b\u56fe\u6240\u793a\u3002 <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/06\/20250618013411-685217935b3de.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u5bf9\u6bd4\u201c\u4ea4\u53c9\u6ce8\u610f\u529b\u201d\u3001\u201c\u5c40\u90e8\u7a97\u53e3\u6ce8\u610f\u529b\u201d\u3001\u201c\u8f74\u65b9\u5411\u6ce8\u610f\u529b\u201d\u4f5c\u8005\u63d0\u51fa\u4e86\u533a\u57df\u6ce8\u610f\u529b&#xff0c;\u5728\u4fdd\u6301\u8f83\u5927\u611f\u53d7\u91ce\u7684\u524d\u63d0\u4e0b\u4e5f\u540c\u65f6\u51cf\u5c11\u4e86\u81ea\u6ce8\u610f\u529b\u8ba1\u7b97\u590d\u6742\u5ea6&#xff0c;\u56fe\u4e2d\u4f5c\u8005\u5c06\u56fe\u7247\u6309\u6c34\u5e73\u6216\u8005\u5782\u76f4\u65b9\u5411\u5206\u6210\u4e864\u5757&#xff0c;\u53ef\u4ee5\u5c06\u539f\u6765\u76842n<sup>2<\/sup>hd\u53d8\u62100.5n<sup>2<\/sup>hd&#xff0c;\u5bf9\u4e8eYOLO\u7684\u8f93\u5165640&#215;640\u800c\u8a00&#xff0c;n\u662f\u56fa\u5b9a\u7684640&#xff0c;\u53ef\u4ee5\u8fbe\u5230\u5b9e\u65f6\u5904\u7406&#xff0c;\u5e76\u4e14\u5bf9\u7cbe\u5ea6\u5f71\u54cd\u4e0d\u5927\u3002<\/p>\n<h3>2.2 \u6b8b\u5dee\u9ad8\u6548\u805a\u5408\u7f51\u7edcResidual Efficient Layer Aggregation Networks<\/h3>\n<p>\u4f5c\u8005\u53c2\u8003\u4e86YOLOv7\u7684ELAN\u7ed3\u6784\u5bf9\u5176\u8fdb\u884c\u6539\u8fdb&#xff0c;\u5982\u4e0b\u56fed\u6240\u793a&#xff1a; <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/06\/20250618013411-6852179381b34.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u4f5c\u8005\u8ba4\u4e3aELAN\u8fd9\u79cd\u67b6\u6784\u53ef\u80fd\u4f1a\u5f15\u5165\u4e0d\u7a33\u5b9a\u6027&#xff0c;\u8fd9\u6837\u7684\u8bbe\u8ba1\u4f1a\u5bfc\u81f4\u68af\u5ea6\u963b\u585e&#xff0c;\u5e76\u4e14\u7f3a\u4e4f\u4ece\u8f93\u5165\u5230\u8f93\u51fa\u7684\u6b8b\u5dee\u8fde\u63a5&#xff0c;\u800c\u4e14\u4e3a\u4e86\u7cbe\u7b80\u8ba1\u7b97&#xff0c;\u4f5c\u8005\u53bb\u6389\u4e86ELAN\u6700\u5f00\u59cb\u7684split\u64cd\u4f5c\u3002<\/p>\n<h3>2.3 \u6574\u4e2aYOLO\u7f51\u7edc\u7ed3\u6784\u4f18\u5316<\/h3>\n<p>1\u3001\u4f5c\u8005\u53d6\u6d88\u4e86backbone\u6700\u540e\u90e8\u5206\u7684\u4e09\u4e2ablocks\u7684\u5806\u53e0&#xff0c;\u8fd9\u70b9\u53ef\u4ee5\u5bf9\u6bd4YOLO11\u7684\u7ed3\u6784\u770b\u51fa\u3002 2\u3001\u4f5c\u8005\u8fd8\u8c03\u6574\u4e86MLP\u7684\u6bd4\u7387&#xff0c;\u75314\u51cf\u5c0f\u52301.2&#xff0c;\u5e76\u4e14\u4e3a\u4e86\u5f97\u5230\u66f4\u9ad8\u6548\u7684\u6027\u80fd&#xff0c;\u5c06\u539f\u6765\u7684nn.Linear&#043;LN\u64cd\u4f5c\u66ff\u6362\u6210\u4e86nn.Conv2d&#043;BN\u64cd\u4f5c\u3002 3\u3001\u4f5c\u8005\u79fb\u9664\u4e86\u4f4d\u7f6e\u7f16\u7801\u64cd\u4f5c&#xff0c;\u589e\u52a0\u4e86\u4e00\u4e2a7&#215;7\u7684\u53ef\u5206\u79bb\u5377\u79ef\u6765\u5e2e\u52a9\u533a\u57df\u6ce8\u610f\u611f\u77e5\u4f4d\u7f6e\u4fe1\u606f\u3002<\/p>\n<hr \/>\n<h2>3\u3001\u4ee3\u7801\u89e3\u6790<\/h2>\n<p>\u5176\u5b9eYOLOv12\u7684\u4e3b\u8981\u521b\u65b0\u70b9\u5c31\u662f\u63d0\u51fa\u4e86\u52a0\u901f\u7684\u533a\u57df\u81ea\u6ce8\u610f\u529b\u673a\u5236&#xff08;Area Attention&#xff09;\u5e76\u4e14\u5e94\u7528\u5728R-ELAN\u6a21\u5757\u4e2d&#xff0c;R-ELAN\u7684\u5b9e\u73b0\u5728\u5b98\u65b9\u4ee3\u7801https:\/\/github.com\/sunsmarterjie\/yolov12\/blob\/main\/ultralytics\/nn\/modules\/block.py\u4e2d&#xff0c;\u5982\u4e0b&#xff1a; <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/06\/20250618013411-68521793a3587.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> A2C2f\u6a21\u5757\u5c31\u662f\u8bba\u6587\u63d0\u5230\u7684R-ELAN\u6a21\u5757&#xff0c;\u5b83\u4e3b\u8981\u5305\u542b\u4e86ABlock&#xff0c;\u4e5f\u5c31\u662f\u8bba\u6587\u4e2d\u7684A2&#xff0c;A2\u5c31\u662f\u533a\u57df\u6ce8\u610f\u529b\u6a21\u5757&#xff0c;\u4ee3\u7801\u5982\u4e0b&#xff1a; <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/06\/20250618013412-685217941c002.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> \u8fd9\u91cc\u53ef\u4ee5\u770b\u5230\u8fd9\u91cc\u7684AAttn\u6a21\u5757\u4f7f\u7528\u4e86area\u53c2\u6570\u8fdb\u884c\u81ea\u6ce8\u610f\u529b\u7684\u8ba1\u7b97&#xff0c;\u800c\u4e14MLP\u4e5f\u662f\u50cf\u8bba\u6587\u4e2d\u63d0\u5230\u7684\u4e00\u6837&#xff0c;\u4f5c\u8005\u6539\u6210\u4e86Conv\u64cd\u4f5c\u3002<\/p>\n<hr \/>\n<h2>4\u3001\u5b9e\u9a8c<\/h2>\n<p>Comparison with State-of-the-arts <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/06\/20250618013412-685217948d40b.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/> 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