{"id":86782,"date":"2026-07-29T08:32:57","date_gmt":"2026-07-29T00:32:57","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/86782.html"},"modified":"2026-07-29T08:32:57","modified_gmt":"2026-07-29T00:32:57","slug":"%e6%95%b0%e6%8d%ae%e9%9b%86%e9%9d%a2%e5%90%91%e6%a1%a5%e6%a2%81%e7%bb%93%e6%9e%84%e8%a1%a8%e8%a7%82%e7%97%85%e5%ae%b3%e8%af%86%e5%88%ab%e3%80%81%e5%83%8f%e7%b4%a0%e7%ba%a7%e5%88%86%e5%89%b2%e4%b8%8e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/86782.html","title":{"rendered":"\u6570\u636e\u96c6\u9762\u5411\u6865\u6881\u7ed3\u6784\u8868\u89c2\u75c5\u5bb3\u8bc6\u522b\u3001\u50cf\u7d20\u7ea7\u5206\u5272\u4e0e\u667a\u80fd\u5de1\u68c0\u4efb\u52a1\uff0c\u8986\u76d6\u88c2\u7f1d\u3001\u8150\u8680\u3001\u6790\u767d\u3001\u6e17\u6f0f\u6c34\u548c\u6df7\u51dd\u571f\u5265\u843d\u4e94\u79cd\u5e38\u89c1\u635f\u4f24\u7c7b\u578b\u3002\u6570\u636e\u540c\u65f6\u63d0\u4f9b\u539f\u59cb\u56fe\u50cf\u3001\u50cf\u7d20\u6807\u7b7e\u3001\u6807\u7b7e\u4fe1\u606f\u548c\u6837\u672c\u6e05\u5355\uff0c\u53ef\u7528\u4e8e\u8bed\u4e49\u5206\u5272\u3001\u75c5\u5bb3\u533a\u57df\u63d0\u53d6\u3001\u635f"},"content":{"rendered":"

\u667a\u6167\u5316-\u6865\u6881\u8868\u9762\u4e94\u7c7b\u75c5\u5bb3\u68c0\u6d4b\u4e0e\u5206\u5272\u6570\u636e\u96c6
\n\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u6570\u636e\u96c6\u9762\u5411\u6865\u6881\u7ed3\u6784\u8868\u89c2\u75c5\u5bb3\u8bc6\u522b\u3001\u50cf\u7d20\u7ea7\u5206\u5272\u4e0e\u667a\u80fd\u5de1\u68c0\u4efb\u52a1，\u8986\u76d6\u88c2\u7f1d\u3001\u8150\u8680\u3001\u6790\u767d\u3001\u6e17\u6f0f\u6c34\u548c\u6df7\u51dd\u571f\u5265\u843d\u4e94\u79cd\u5e38\u89c1\u635f\u4f24\u7c7b\u578b\u3002\u6570\u636e\u540c\u65f6\u63d0\u4f9b\u539f\u59cb\u56fe\u50cf\u3001\u50cf\u7d20\u6807\u7b7e\u3001\u6807\u7b7e\u4fe1\u606f\u548c\u6837\u672c\u6e05\u5355，\u53ef\u7528\u4e8e\u8bed\u4e49\u5206\u5272\u3001\u75c5\u5bb3\u533a\u57df\u63d0\u53d6\u3001\u635f\u4f24\u9762\u79ef\u8bc4\u4f30\u53ca\u5de1\u68c0\u7b97\u6cd5\u7814\u7a76\u3002\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u56fe\u50cf\u6570\u91cf：5,821\u5f20
\n\u8150\u8680\u56fe\u50cf：955\u5f20
\n\u88c2\u7f1d\u56fe\u50cf：1,480\u5f20
\n\u6790\u767d\u56fe\u50cf：1,269\u5f20
\n\u6e17\u6f0f\u6c34\u56fe\u50cf：873\u5f20
\n\u5265\u843d\u56fe\u50cf：1,244\u5f20
\n\u7c7b\u522b\u6570\u91cf：5\u7c7b\u75c5\u5bb3，\u53e6\u542b\u80cc\u666f\u6807\u7b7e
\n\u6807\u6ce8\u7c7b\u578b：\u50cf\u7d20\u7ea7\u8bed\u4e49\u5206\u5272\u6807\u7b7e
\n\u914d\u5957\u6587\u4ef6：\u539f\u59cb\u56fe\u50cf\u3001\u6807\u7b7e\u56fe\u3001JSON\u6807\u7b7e\u8bf4\u660e\u3001\u6837\u672c\u5217\u8868
\n\u6570\u636e\u4f53\u79ef：\u7ea62.07G
\n\u9002\u7528\u4efb\u52a1：\u6865\u6881\u75c5\u5bb3\u5206\u5272\u3001\u7ed3\u6784\u5de1\u68c0\u3001\u635f\u4f24\u533a\u57df\u5b9a\u4f4d\u3001\u5de5\u7a0b\u89c6\u89c9\u5206\u6790<\/p>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u6881\u8868\u9762\u4e94\u7c7b\u75c5\u5bb3\u68c0\u6d4b\u4e0e\u5206\u5272\u6570\u636e\u96c6\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/h2>\n

\u6570\u636e\u96c6\u4fe1\u606f\u8868<\/h3>\n\n\u9879\u76ee\u8be6\u7ec6\u8bf4\u660e<\/tr>\n\n\n\n\n\n\n\n\n\n\n
\u6570\u636e\u96c6\u540d\u79f0<\/td>\n\u6881\u8868\u9762\u4e94\u7c7b\u75c5\u5bb3\u68c0\u6d4b\u4e0e\u5206\u5272\u6570\u636e\u96c6<\/td>\n<\/tr>\n
\u56fe\u50cf\u603b\u91cf<\/td>\n5821\u5f20<\/td>\n<\/tr>\n
\u5404\u7c7b\u75c5\u5bb3\u6837\u672c\u5206\u5e03<\/td>\n\u8150\u8680：955\u5f20\u88c2\u7f1d：1480\u5f20\u6790\u767d：1269\u5f20\u6e17\u6f0f\u6c34：873\u5f20\u6df7\u51dd\u571f\u5265\u843d：1244\u5f20<\/td>\n<\/tr>\n
\u5206\u5272\u7c7b\u522b<\/td>\n5\u7c7b\u75c5\u5bb3 + \u80cc\u666fcrack（\u88c2\u7f1d）\u3001corrosion（\u8150\u8680）\u3001efflorescence（\u6790\u767d）\u3001water_seepage（\u6e17\u6f0f\u6c34）\u3001spalling（\u6df7\u51dd\u571f\u5265\u843d）\u3001background（\u80cc\u666f）<\/td>\n<\/tr>\n
\u6807\u6ce8\u7c7b\u578b<\/td>\n\u50cf\u7d20\u7ea7\u8bed\u4e49\u5206\u5272\u6807\u6ce8<\/td>\n<\/tr>\n
\u914d\u5957\u6587\u4ef6<\/td>\n\u539f\u59cb\u56fe\u50cf\u3001\u5206\u5272\u6807\u7b7e\u56fe\u3001JSON\u6807\u7b7e\u8bf4\u660e\u3001\u6837\u672c\u6e05\u5355<\/td>\n<\/tr>\n
\u6570\u636e\u5927\u5c0f<\/td>\n\u7ea62.07GB<\/td>\n<\/tr>\n
\u9002\u914d\u4efb\u52a1<\/td>\n\u6865\u6881\u8868\u89c2\u75c5\u5bb3\u8bed\u4e49\u5206\u5272\u3001\u75c5\u5bb3\u533a\u57df\u63d0\u53d6\u3001\u635f\u4f24\u9762\u79ef\u6d4b\u7b97\u3001\u6865\u6881\u7ed3\u6784\u667a\u80fd\u5de1\u68c0\u3001\u5de5\u7a0b\u7ed3\u6784\u89c6\u89c9\u7b97\u6cd5\u7814\u53d1<\/td>\n<\/tr>\n
\u63a8\u8350\u6a21\u578b<\/td>\nYOLOv8-seg \/ YOLOv11-seg\u3001U-Net\u3001SegFormer\u3001Mask R-CNN<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5206\u5272\u4efb\u52a1yaml\u914d\u7f6e（YOLO-seg \u6807\u51c6\u683c\u5f0f bridge_defect_seg.yaml）<\/h3>\n

path<\/span>:<\/span> .\/bridge_surface_defect_dataset
\ntrain<\/span>:<\/span> images\/train
\nval<\/span>:<\/span> images\/val
\ntest<\/span>:<\/span> images\/test<\/p>\n

nc<\/span>:<\/span> 5<\/span>
\nnames<\/span>:<\/span>
\n 0<\/span>:<\/span> crack
\n 1<\/span>:<\/span> corrosion
\n 2<\/span>:<\/span> efflorescence
\n 3<\/span>:<\/span> water_seepage
\n 4<\/span>:<\/span> spalling<\/p>\n

\u73af\u5883\u4f9d\u8d56<\/h3>\n

pip install<\/span> ultralytics torch opencv-python pillow numpy<\/p>\n

YOLOv8-seg \u8bad\u7ec3\u4ee3\u7801 train_bridge_seg.py<\/h3>\n

from<\/span> ultralytics import<\/span> YOLO<\/p>\n

def<\/span> main<\/span>(<\/span>)<\/span>:<\/span>
\n # \u52a0\u8f7d\u5206\u5272\u9884\u8bad\u7ec3\u6743\u91cd，\u517c\u5bb9 YOLOv8-seg \/ YOLOv11-seg<\/span>
\n model =<\/span> YOLO(<\/span>"yolov8s-seg.pt"<\/span>)<\/span><\/p>\n

train_result =<\/span> model.<\/span>train(<\/span>
\n data=<\/span>".\/bridge_surface_defect_dataset\/bridge_defect_seg.yaml"<\/span>,<\/span>
\n epochs=<\/span>100<\/span>,<\/span>
\n imgsz=<\/span>640<\/span>,<\/span>
\n batch=<\/span>12<\/span>,<\/span>
\n device=<\/span>0<\/span>,<\/span>
\n workers=<\/span>4<\/span>,<\/span>
\n patience=<\/span>15<\/span>,<\/span>
\n project=<\/span>"runs\/train"<\/span>,<\/span>
\n name=<\/span>"bridge_defect_segment"<\/span>,<\/span>
\n exist_ok=<\/span>True<\/span>
\n )<\/span>
\n print<\/span>(<\/span>"\u8bad\u7ec3\u5b8c\u6210，\u6743\u91cd\u4fdd\u5b58\u8def\u5f84：runs\/train\/bridge_defect_segment\/weights"<\/span>)<\/span>
\n metrics =<\/span> model.<\/span>val(<\/span>split=<\/span>"test"<\/span>)<\/span>
\n print<\/span>(<\/span>f"mAP@50: <\/span>{<\/span>metrics.<\/span>box.<\/span>map50:<\/span>.4f<\/span>}<\/span><\/span>"<\/span><\/span>)<\/span><\/p>\n

if<\/span> __name__ ==<\/span> "__main__"<\/span>:<\/span>
\n main(<\/span>)<\/span><\/p>\n

\u5206\u5272\u63a8\u7406\u4ee3\u7801 predict_bridge_seg.py<\/h3>\n

from<\/span> ultralytics import<\/span> YOLO
\nimport<\/span> cv2<\/p>\n

model =<\/span> YOLO(<\/span>"runs\/train\/bridge_defect_segment\/weights\/best.pt"<\/span>)<\/span><\/p>\n

if<\/span> __name__ ==<\/span> "__main__"<\/span>:<\/span>
\n img_file =<\/span> "bridge_test.jpg"<\/span>
\n results =<\/span> model.<\/span>predict(<\/span>
\n source=<\/span>img_file,<\/span>
\n conf=<\/span>0.25<\/span>,<\/span>
\n save=<\/span>True<\/span>,<\/span>
\n save_masks=<\/span>True<\/span> # \u8f93\u51fa\u5355\u72ec\u75c5\u5bb3\u63a9\u7801\u56fe，\u7528\u4e8e\u9762\u79ef\u8ba1\u7b97<\/span>
\n )<\/span>
\n vis_img =<\/span> results[<\/span>0<\/span>]<\/span>.<\/span>plot(<\/span>)<\/span>
\n cv2.<\/span>imshow(<\/span>"Bridge Defect Segmentation"<\/span>,<\/span> vis_img)<\/span>
\n cv2.<\/span>waitKey(<\/span>0<\/span>)<\/span>
\n cv2.<\/span>destroyAllWindows(<\/span>)<\/span><\/p>\n

\u5173\u952e\u8bcd\u6807\u7b7e<\/h3>\n

#\u6865\u6881\u75c5\u5bb3\u5206\u5272 #\u6df7\u51dd\u571f\u88c2\u7f1d\u68c0\u6d4b #\u6865\u6881\u667a\u80fd\u5de1\u68c0 #\u8bed\u4e49\u5206\u5272\u6570\u636e\u96c6 #\u7ed3\u6784\u8868\u89c2\u635f\u4f24\u8bc6\u522b #YOLO-seg\u6570\u636e\u96c6<\/p>\n

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u4e2d\u82f1\u672f\u8bed\u5bf9\u7167\u8868（\u6807\u6ce8\u901a\u7528）<\/h4>\n\n\u4e2d\u6587\u75c5\u5bb3\u540d\u79f0\u6807\u51c6\u82f1\u6587\u6807\u7b7e<\/tr>\n\n\n\n\n\n\n
\u88c2\u7f1d<\/td>\ncrack<\/td>\n<\/tr>\n
\u8150\u8680<\/td>\ncorrosion<\/td>\n<\/tr>\n
\u6790\u767d（\u6cdb\u78b1）<\/td>\nefflorescence<\/td>\n<\/tr>\n
\u6e17\u6f0f\u6c34<\/td>\nwater_seepage<\/td>\n<\/tr>\n
\u6df7\u51dd\u571f\u5265\u843d<\/td>\nspalling<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

\u667a\u6167\u5316-\u6865\u6881\u8868\u9762\u4e94\u7c7b\u75c5\u5bb3\u68c0\u6d4b\u4e0e\u5206\u5272\u6570\u636e\u96c6\u6570\u636e\u96c6\u9762\u5411\u6865\u6881\u7ed3\u6784\u8868\u89c2\u75c5\u5bb3\u8bc6\u522b\u3001\u50cf\u7d20\u7ea7\u5206\u5272\u4e0e\u667a\u80fd\u5de1\u68c0\u4efb\u52a1，\u8986\u76d6\u88c2\u7f1d\u3001\u8150\u8680\u3001\u6790\u767d\u3001\u6e17\u6f0f\u6c34\u548c\u6df7\u51dd\u571f\u5265\u843d\u4e94\u79cd\u5e38\u89c1\u635f\u4f24\u7c7b\u578b\u3002\u6570\u636e\u540c\u65f6\u63d0\u4f9b\u539f\u59cb\u56fe\u50cf\u3001\u50cf\u7d20\u6807\u7b7e\u3001\u6807\u7b7e\u4fe1\u606f\u548c\u6837\u672c\u6e05\u5355，\u53ef\u7528\u4e8e\u8bed\u4e49\u5206\u5272\u3001\u75c5\u5bb3\u533a\u57df\u63d0\u53d6\u3001\u635f\u4f24\u9762\u79ef\u8bc4\u4f30\u53ca\u5de1\u68c0\u7b97\u6cd5\u7814\u7a76\u3002\u56fe\u50cf\u6570\u91cf：5,821\u5f20
\n\u8150\u8680\u56fe\u50cf：955\u5f20
\n\u88c2\u7f1d\u56fe\u50cf：1,480\u5f20
\n\u6790\u767d\u56fe\u50cf：1,269\u5f20
\n\u6e17\u6f0f\u6c34\u56fe\u50cf：873\u5f20
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