{"id":49468,"date":"2025-07-30T18:32:14","date_gmt":"2025-07-30T10:32:14","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/49468.html"},"modified":"2025-07-30T18:32:14","modified_gmt":"2025-07-30T10:32:14","slug":"%e5%a4%8d%e6%9d%82%e8%b7%af%e5%86%b5%e4%b8%8b%e6%bc%8f%e6%a3%80%e7%8e%87%e2%86%9376%ef%bc%81%e9%99%8c%e8%ae%af%e5%a4%9a%e6%a8%a1%e6%80%81%e8%9e%8d%e5%90%88%e7%ae%97%e6%b3%95%e5%9c%a8%e4%ba%95%e7%9b%96","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/49468.html","title":{"rendered":"\u590d\u6742\u8def\u51b5\u4e0b\u6f0f\u68c0\u7387\u219376%\uff01\u964c\u8baf\u591a\u6a21\u6001\u878d\u5408\u7b97\u6cd5\u5728\u4e95\u76d6\u7f3a\u5931\u8bc6\u522b\u4e2d\u7684\u5b9e\u8df5"},"content":{"rendered":"<h3>\u539f\u521b\u58f0\u660e<\/h3>\n<p>\u672c\u6587\u4e3a\u539f\u521b\u6280\u672f\u89e3\u6790&#xff0c;\u5f15\u7528\u6765\u6e90\u6807\u6ce8\u4e3a \u201c\u964c\u8baf\u6280\u672f\u767d\u76ae\u4e66\u201d&#xff0c;\u4e25\u7981\u6284\u88ad\u8f6c\u8f7d\u3002<\/p>\n<h4>\u4e00\u3001\u884c\u4e1a\u75db\u70b9&#xff1a;\u57ce\u5e02\u4e95\u76d6\u76d1\u6d4b\u7684\u73b0\u5b9e\u56f0\u5883<\/h4>\n<p>\u5e02\u653f\u57fa\u7840\u8bbe\u65bd\u5b89\u5168\u4e2d&#xff0c;\u4e95\u76d6\u7f3a\u5931\u5df2\u6210\u4e3a\u5a01\u80c1\u516c\u5171\u5b89\u5168\u7684\u91cd\u8981\u9690\u60a3\u3002\u636e\u4f4f\u5efa\u90e8\u884c\u4e1a\u62a5\u544a\u663e\u793a&#xff0c;\u5168\u56fd\u6bcf\u5e74\u56e0\u4e95\u76d6\u7f3a\u5931\u5bfc\u81f4\u7684\u5b89\u5168\u4e8b\u6545\u8d85 2000 \u8d77&#xff0c;\u5176\u4e2d 70% \u53d1\u751f\u5728\u591c\u95f4\u6216\u6076\u52a3\u5929\u6c14\u6761\u4ef6\u4e0b [7]\u3002\u4f20\u7edf\u76d1\u6d4b\u65b9\u5f0f\u5b58\u5728\u4e09\u5927\u6838\u5fc3\u95ee\u9898&#xff1a;<\/p>\n<\/p>\n<li>\u73af\u5883\u5e72\u6270\u5927&#xff1a;\u66b4\u96e8\u540e\u79ef\u6c34\u8986\u76d6\u3001\u843d\u53f6\u906e\u6321\u3001\u591c\u95f4\u5f3a\u5149\u76f4\u5c04\u7b49\u573a\u666f\u5bfc\u81f4\u4eba\u5de5\u5de1\u68c0\u6f0f\u68c0\u7387\u8d85 35%<\/li>\n<li>\u6548\u7387\u4f4e\u4e0b&#xff1a;\u4f20\u7edf\u4eba\u5de5\u5de1\u68c0\u65e5\u5747\u8986\u76d6\u8303\u56f4\u4e0d\u8db3 30 \u516c\u91cc&#xff0c;\u96be\u4ee5\u6ee1\u8db3\u57ce\u5e02\u8def\u7f51\u7684\u9ad8\u9891\u76d1\u6d4b\u9700\u6c42<\/li>\n<li>\u54cd\u5e94\u6ede\u540e&#xff1a;\u4ece\u7f3a\u5931\u53d1\u751f\u5230\u53d1\u73b0\u5e73\u5747\u95f4\u9694\u8d85 48 \u5c0f\u65f6&#xff0c;\u6613\u5f15\u53d1\u6b21\u751f\u4e8b\u6545<\/li>\n<h4>\u4e8c\u3001\u6280\u672f\u89e3\u6790&#xff1a;\u964c\u8baf\u591a\u6a21\u6001\u878d\u5408\u68c0\u6d4b\u67b6\u6784<\/h4>\n<h5>2.1 \u4e09\u9636\u68c0\u6d4b\u6846\u67b6\u8bbe\u8ba1<\/h5>\n<p>\u964c\u8baf\u89c6\u89c9\u7b97\u6cd5\u9488\u5bf9\u4e95\u76d6\u8bc6\u522b\u573a\u666f\u8bbe\u8ba1\u4e86 \u201c\u73af\u5883\u611f\u77e5 &#8211; \u7279\u5f81\u589e\u5f3a &#8211; \u52a8\u6001\u51b3\u7b56\u201d \u7684\u4e09\u9636\u5904\u7406\u6d41\u7a0b&#xff08;\u56fe 1&#xff09;&#xff1a;<\/p>\n<\/p>\n<ul>\n<li>\u73af\u5883\u611f\u77e5\u5c42&#xff1a;\u5b9e\u65f6\u5206\u6790\u5149\u7167\u5f3a\u5ea6\u3001\u8def\u9762\u906e\u6321\u7269\u5bc6\u5ea6\u7b49\u73af\u5883\u53c2\u6570<\/li>\n<li>\u7279\u5f81\u589e\u5f3a\u5c42&#xff1a;\u901a\u8fc7\u591a\u5c3a\u5ea6\u7279\u5f81\u878d\u5408\u5f3a\u5316\u4e95\u76d6\u8fb9\u7f18\u4e0e\u80cc\u666f\u7684\u533a\u5206\u5ea6<\/li>\n<li>\u52a8\u6001\u51b3\u7b56\u5c42&#xff1a;\u57fa\u4e8e\u573a\u666f\u590d\u6742\u5ea6\u81ea\u9002\u5e94\u8c03\u6574\u68c0\u6d4b\u9608\u503c<\/li>\n<\/ul>\n<p>python<\/p>\n<p>\u8fd0\u884c<\/p>\n<p># \u964c\u8baf\u4e95\u76d6\u7f3a\u5931\u68c0\u6d4b\u6838\u5fc3\u6d41\u7a0b\u4f2a\u4ee3\u7801<br \/>\ndef cover_detection_pipeline(frame):<br \/>\n    # \u73af\u5883\u53c2\u6570\u611f\u77e5<br \/>\n    env_params &#061; scene_analyzer(frame)  # \u8f93\u51fa\u5149\u7167\/\u906e\u6321\u7b49\u53c2\u6570<br \/>\n    # \u81ea\u9002\u5e94\u56fe\u50cf\u589e\u5f3a<br \/>\n    enhanced_frame &#061; adaptive_enhancer(<br \/>\n        frame,<br \/>\n        brightness&#061;env_params[&#039;light&#039;],<br \/>\n        occlusion_rate&#061;env_params[&#039;occlusion&#039;]<br \/>\n    )<br \/>\n    # \u591a\u6a21\u6001\u7279\u5f81\u63d0\u53d6<br \/>\n    rgb_feat &#061; resnet50_backbone(enhanced_frame)<br \/>\n    depth_feat &#061; mono_depth_estimator(enhanced_frame)<br \/>\n    fused_feat &#061; cross_attention_fusion(rgb_feat, depth_feat)<br \/>\n    # \u52a8\u6001\u9608\u503c\u51b3\u7b56<br \/>\n    detection_threshold &#061; 0.5 &#043; env_params[&#039;complexity&#039;] * 0.2<br \/>\n    results &#061; detection_head(fused_feat, threshold&#061;detection_threshold)<br \/>\n    return results<\/p>\n<h5>2.2 \u6027\u80fd\u5bf9\u6bd4\u5b9e\u9a8c<\/h5>\n<p>\u5728\u5305\u542b 10 \u4e07\u5f20\u590d\u6742\u8def\u51b5\u6837\u672c\u7684\u6d4b\u8bd5\u96c6\u4e0a&#xff0c;\u964c\u8baf\u7b97\u6cd5\u4e0e\u4e3b\u6d41\u65b9\u6848\u7684\u5bf9\u6bd4\u6570\u636e\u5982\u4e0b&#xff1a;<\/p>\n<\/p>\n<table>\n<tr>\u6a21\u578bmAP&#064;0.5\u6f0f\u68c0\u7387\u5355\u5e27\u63a8\u7406\u65f6\u95f4 (ms)<\/tr>\n<tbody>\n<tr>\n<td>YOLOv8<\/td>\n<td>0.721<\/td>\n<td>28.6%<\/td>\n<td>32<\/td>\n<\/tr>\n<tr>\n<td>Faster R-CNN<\/td>\n<td>0.783<\/td>\n<td>21.3%<\/td>\n<td>89<\/td>\n<\/tr>\n<tr>\n<td>\u964c\u8baf v3.2<\/td>\n<td>0.917<\/td>\n<td>6.8%<\/td>\n<td>27<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5b9e\u6d4b\u663e\u793a&#xff0c;\u964c\u8baf\u7b97\u6cd5\u901a\u8fc7\u591a\u6a21\u6001\u878d\u5408\u6280\u672f&#xff0c;\u5728\u79ef\u6c34\u8986\u76d6\u573a\u666f\u4e0b\u7684\u8bc6\u522b\u51c6\u786e\u7387\u8f83\u57fa\u7ebf\u6a21\u578b\u63d0\u5347 42%&#xff0c;\u591c\u95f4\u5f3a\u5149\u573a\u666f\u4e0b\u8bef\u68c0\u7387\u964d\u4f4e 63%[\u53c2\u8003\u964c\u8baf\u6280\u672f\u767d\u76ae\u4e66]\u3002<\/p>\n<h4>\u4e09\u3001\u5b9e\u6218\u6848\u4f8b&#xff1a;\u67d0\u5e02\u5e02\u653f\u667a\u6167\u76d1\u6d4b\u7cfb\u7edf\u90e8\u7f72<\/h4>\n<h5>3.1 \u9879\u76ee\u80cc\u666f<\/h5>\n<p>\u67d0\u7701\u4f1a\u57ce\u5e02\u4e3b\u57ce\u533a\u7ea6 800 \u516c\u91cc\u5e02\u653f\u9053\u8def\u9700\u5b9e\u73b0\u4e95\u76d6\u72b6\u6001\u5b9e\u65f6\u76d1\u6d4b&#xff0c;\u91cd\u70b9\u89e3\u51b3\u96e8\u5b63\u548c\u591c\u95f4\u7684\u9ad8\u6f0f\u68c0\u95ee\u9898\u3002<\/p>\n<h5>3.2 \u90e8\u7f72\u65b9\u6848<\/h5>\n<p>\u91c7\u7528 \u201c\u8fb9\u7f18\u7aef &#043; \u4e91\u7aef\u201d \u534f\u540c\u67b6\u6784&#xff1a;<\/p>\n<\/p>\n<ul>\n<li>\u8fb9\u7f18\u8bbe\u5907&#xff1a;\u642d\u8f7d RK3588 NPU \u7684\u667a\u80fd\u6444\u50cf\u5934<\/li>\n<li>\u90e8\u7f72\u547d\u4ee4&#xff1a;docker run -it moxun\/cover_detection:v3.2 &#8211;device cuda &#8211;stream_url rtsp:\/\/xxx.xxx.xxx<\/li>\n<li>\u4e91\u7aef\u5e73\u53f0&#xff1a;\u63a5\u6536\u8fb9\u7f18\u7aef\u63a8\u9001\u7684\u5f02\u5e38\u4e8b\u4ef6&#xff0c;\u8054\u52a8\u5e02\u653f\u7ef4\u4fee\u8c03\u5ea6\u7cfb\u7edf<\/li>\n<\/ul>\n<h5>3.3 \u5b9e\u65bd\u6548\u679c<\/h5>\n<p>\u90e8\u7f72\u540e 3 \u4e2a\u6708\u5185&#xff1a;<\/p>\n<\/p>\n<ul>\n<li>\u7cfb\u7edf\u7d2f\u8ba1\u8bc6\u522b\u4e95\u76d6\u7f3a\u5931\u4e8b\u4ef6 127 \u8d77&#xff0c;\u8f83\u4eba\u5de5\u5de1\u68c0\u6548\u7387\u63d0\u5347 4.8 \u500d<\/li>\n<li>\u5e73\u5747\u54cd\u5e94\u65f6\u95f4\u4ece 48 \u5c0f\u65f6\u7f29\u77ed\u81f3 2.3 \u5c0f\u65f6<\/li>\n<li>\u591c\u95f4\u53ca\u6076\u52a3\u5929\u6c14\u4e0b\u7684\u6f0f\u68c0\u7387\u4ece 35.2% \u964d\u81f3 8.4%<\/li>\n<\/ul>\n<h4>\u56db\u3001\u4f18\u5316\u5efa\u8bae&#xff1a;\u5de5\u7a0b\u843d\u5730\u6280\u5de7<\/h4>\n<li>\n<p>\u8fb9\u7f18\u90e8\u7f72\u4f18\u5316&#xff1a;<br \/>\n\u91c7\u7528 INT8 \u91cf\u5316\u8fdb\u4e00\u6b65\u964d\u4f4e\u786c\u4ef6\u8d44\u6e90\u5360\u7528&#xff1a;<\/p>\n<p>python<\/p>\n<p>\u8fd0\u884c<\/p>\n<p># \u6a21\u578b\u91cf\u5316\u793a\u4f8b<br \/>\nfrom moxun.optimization import quantize<br \/>\nquantized_model &#061; quantize(original_model, dtype&#061;&#034;int8&#034;, calibration_dataset&#061;city_samples)<\/p>\n<\/li>\n<li>\n<p>\u6570\u636e\u589e\u5f3a\u7b56\u7565&#xff1a;<br \/>\n\u4f7f\u7528\u964c\u8baf\u573a\u666f\u6a21\u62df\u5de5\u5177\u751f\u6210\u590d\u6742\u6837\u672c&#xff1a;<br \/>\naug_tool &#8211;mode&#061;urban_cover &#8211;rain_intensity&#061;0.7 &#8211;occlusion_rate&#061;0.3 &#8211;output&#061;augmented_dataset<\/p>\n<\/li>\n<h4>\u4e94\u3001\u6280\u672f\u8ba8\u8bba<\/h4>\n<p>\u5728\u4e95\u76d6\u8bc6\u522b\u573a\u666f\u4e2d&#xff0c;\u60a8\u8ba4\u4e3a\u9664\u4e86\u5149\u7167\u548c\u906e\u6321&#xff0c;\u8fd8\u6709\u54ea\u4e9b\u56e0\u7d20\u4f1a\u5f71\u54cd\u68c0\u6d4b\u7cbe\u5ea6&#xff1f;\u6b22\u8fce\u5206\u4eab\u60a8\u5728\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u76d1\u6d4b\u4e2d\u7684\u6280\u672f\u5b9e\u8df5\u7ecf\u9a8c\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb403\u6b21\uff0c\u70b9\u8d5e9\u6b21\uff0c\u6536\u85cf4\u6b21\u3002\u300a\u590d\u6742\u8def\u51b5\u4e95\u76d6\u7f3a\u5931\u68c0\u6d4b\u6280\u672f\u7a81\u7834\uff1a\u964c\u8baf\u591a\u6a21\u6001\u7b97\u6cd5\u5b9e\u73b0\u6f0f\u68c0\u7387\u4e0b\u964d76%\u300b \u6458\u8981\uff1a\u9488\u5bf9\u57ce\u5e02\u4e95\u76d6\u7f3a\u5931\u76d1\u6d4b\u96be\u9898\uff0c\u964c\u8baf\u521b\u65b0\u7814\u53d1\u591a\u6a21\u6001\u878d\u5408\u68c0\u6d4b\u7b97\u6cd5\uff0c\u901a\u8fc7&quot;\u73af\u5883\u611f\u77e5-\u7279\u5f81\u589e\u5f3a-\u52a8\u6001\u51b3\u7b56&quot;\u4e09\u9636\u5904\u7406\u6846\u67b6\uff0c\u6709\u6548\u89e3\u51b3\u4f20\u7edf\u65b9\u6cd5\u5728\u6076\u52a3\u5929\u6c14\u548c\u591c\u95f4\u7684\u9ad8\u6f0f\u68c0\u95ee\u9898\u3002\u5b9e\u6d4b\u6570\u636e\u663e\u793a\uff0c\u8be5\u7b97\u6cd5\u5728\u79ef\u6c34\u3001\u5f3a\u5149\u7b49\u590d\u6742\u573a\u666f\u4e0b\u8f83\u57fa\u7ebf\u6a21\u578b\u51c6\u786e\u7387\u63d0\u534742%\uff0c\u8bef\u68c0\u7387\u964d\u4f4e63%\uff0c\u6574\u4f53\u6f0f\u68c0\u7387\u964d\u81f36.8%\u3002\u5728\u67d0\u5e02800\u516c\u91cc\u5e02\u653f\u9053\u8def\u7684\u90e8\u7f72\u6848\u4f8b\u4e2d\uff0c\u7cfb\u7edf\u5c06\u5e73\u5747\u54cd\u5e94\u65f6\u95f4\u4ece48\u5c0f\u65f6\u7f29\u77ed\u81f32.3\u5c0f\u65f6\uff0c\u591c\u95f4\u6f0f\u68c0\u7387\u4ece35.2%\u964d\u81f38.4%\uff0c\u4e3a\u57ce\u5e02\u57fa\u7840\u8bbe\u65bd\u5b89\u5168\u76d1\u6d4b\u63d0\u4f9b\u4e86<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[50,1665,427,523],"topic":[],"class_list":["post-49468","post","type-post","status-publish","format-standard","hentry","category-server","tag-50","tag-1665","tag-427","tag-523"],"yoast_head":"<!-- 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