<annotation>
<folder>images</folder>
<filename>car_plat4_32.jpg</filename>
<path>E:\images_data\images\car_plat4_32.jpg</path>
<source>
<database>Unknown</database>
</source>
<size>
<width>1360</width>
<height>768</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>500</xmin>
<ymin>214</ymin>
<xmax>735</xmax>
<ymax>429</ymax>
</bndbox>
</object>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>534</xmin>
<ymin>474</ymin>
<xmax>830</xmax>
<ymax>768</ymax>
</bndbox>
</object>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>1053</xmin>
<ymin>403</ymin>
<xmax>1360</xmax>
<ymax>660</ymax>
</bndbox>
</object>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>797</xmin>
<ymin>79</ymin>
<xmax>975</xmax>
<ymax>291</ymax>
</bndbox>
</object>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>438</xmin>
<ymin>49</ymin>
<xmax>608</xmax>
<ymax>188</ymax>
</bndbox>
</object>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>533</xmin>
<ymin>1</ymin>
<xmax>654</xmax>
<ymax>39</ymax>
</bndbox>
</object>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>373</xmin>
<ymin>1</ymin>
<xmax>512</xmax>
<ymax>90</ymax>
</bndbox>
</object>
<object>
<name>plat</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>602</xmin>
<ymin>375</ymin>
<xmax>654</xmax>
<ymax>394</ymax>
</bndbox>
</object>
<object>
<name>plat</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>508</xmin>
<ymin>151</ymin>
<xmax>547</xmax>
<ymax>163</ymax>
</bndbox>
</object>
<object>
<name>plat</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>429</xmin>
<ymin>53</ymin>
<xmax>466</xmax>
<ymax>63</ymax>
</bndbox>
</object>
<object>
<name>plat</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>570</xmin>
<ymin>8</ymin>
<xmax>603</xmax>
<ymax>16</ymax>
</bndbox>
</object>
<object>
<name>plat</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>878</xmin>
<ymin>244</ymin>
<xmax>921</xmax>
<ymax>257</ymax>
</bndbox>
</object>
<object>
<name>plat</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>1222</xmin>
<ymin>589</ymin>
<xmax>1288</xmax>
<ymax>608</ymax>
</bndbox>
</object>
</annotation>
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车辆车牌检测数据集-1456张(道路监控拍摄,附voc和yolo两种格式标签)
共4372个文件
xml:1458个
txt:1458个
jpg:1456个
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【实际项目应用】:车牌检测、车牌识别、车辆检测 【数据集说明】:车辆车牌检测数据集,一共1456张图片,标签包含两类,分别为“car”和“plat”,道路监控摄像头视角背景,数据分布均匀,标签包含voc(xml)、yolo(txt)两种格式,多种目标检测算法可直接使用。纯手工标注,标注精准,算法拟合不错,数据质量可靠。 【更多数据集介绍】https://blog.csdn.net/DeepLearning_/article/details/127276492?spm=1001.2014.3001.5502 【重要提醒】 1、csdn上资源保证是完整最新,会不定期更新优化; 2、通过第三方代下载,而不是直接自己账号在csdn官方下载,博主不对下载的资源作任何保证,且不提供任何形式的技术支持和答疑!!!
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车辆车牌检测数据集-1456张(道路监控拍摄,附voc和yolo两种格式标签) (4372个子文件)
car_plat3_775.jpg 553KB
car_plat3_811.jpg 550KB
car_plat1_11.jpg 548KB
car_plat3_779.jpg 548KB
car_plat3_175.jpg 548KB
car_plat3_767.jpg 546KB
car_plat3_769.jpg 546KB
car_plat2_20.jpg 545KB
car_plat3_977.jpg 545KB
car_plat3_981.jpg 545KB
car_plat3_821.jpg 544KB
car_plat3_777.jpg 543KB
car_plat3_813.jpg 543KB
car_plat3_979.jpg 543KB
car_plat3_823.jpg 542KB
car_plat3_780.jpg 542KB
car_plat1_39.jpg 541KB
car_plat3_499.jpg 541KB
car_plat1_10.jpg 540KB
car_plat1_77.jpg 540KB
car_plat2_22.jpg 540KB
car_plat3_815.jpg 540KB
car_plat3_755.jpg 540KB
car_plat3_176.jpg 539KB
car_plat3_513.jpg 539KB
car_plat1_9.jpg 539KB
car_plat3_776.jpg 539KB
car_plat2_21.jpg 538KB
car_plat3_770.jpg 538KB
car_plat3_501.jpg 538KB
car_plat1_59.jpg 538KB
car_plat3_753.jpg 537KB
car_plat3_174.jpg 537KB
car_plat3_500.jpg 537KB
car_plat3_768.jpg 537KB
car_plat3_381.jpg 537KB
car_plat3_774.jpg 537KB
car_plat3_845.jpg 537KB
car_plat3_978.jpg 537KB
car_plat3_761.jpg 536KB
car_plat3_766.jpg 536KB
car_plat3_173.jpg 536KB
car_plat3_812.jpg 536KB
car_plat2_12.jpg 536KB
car_plat2_18.jpg 536KB
car_plat3_675.jpg 536KB
car_plat3_577.jpg 535KB
car_plat3_611.jpg 534KB
car_plat3_163.jpg 534KB
car_plat2_16.jpg 534KB
car_plat3_162.jpg 534KB
car_plat2_4.jpg 534KB
car_plat3_399.jpg 534KB
car_plat3_609.jpg 534KB
car_plat1_64.jpg 533KB
car_plat1_19.jpg 533KB
car_plat3_65.jpg 532KB
car_plat3_483.jpg 532KB
car_plat3_512.jpg 532KB
car_plat3_759.jpg 532KB
car_plat3_976.jpg 532KB
car_plat1_38.jpg 532KB
car_plat3_164.jpg 532KB
car_plat3_124.jpg 531KB
car_plat2_590.jpg 531KB
car_plat3_168.jpg 531KB
car_plat3_498.jpg 530KB
car_plat3_822.jpg 530KB
car_plat3_781.jpg 530KB
car_plat2_246.jpg 530KB
car_plat3_849.jpg 530KB
car_plat3_820.jpg 530KB
car_plat1_63.jpg 530KB
car_plat2_248.jpg 530KB
car_plat2_530.jpg 530KB
car_plat3_610.jpg 529KB
car_plat3_841.jpg 529KB
car_plat2_532.jpg 529KB
car_plat3_481.jpg 529KB
car_plat3_847.jpg 529KB
car_plat3_167.jpg 529KB
car_plat3_735.jpg 529KB
car_plat3_677.jpg 529KB
car_plat3_765.jpg 529KB
car_plat3_653.jpg 529KB
car_plat2_19.jpg 529KB
car_plat3_337.jpg 529KB
car_plat3_787.jpg 529KB
car_plat1_79.jpg 529KB
car_plat3_342.jpg 529KB
car_plat3_754.jpg 529KB
car_plat2_602.jpg 529KB
car_plat2_10.jpg 529KB
car_plat3_758.jpg 529KB
car_plat3_734.jpg 529KB
car_plat3_576.jpg 528KB
car_plat3_756.jpg 528KB
car_plat3_673.jpg 528KB
car_plat2_604.jpg 528KB
car_plat3_425.jpg 528KB
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