数据集——个人收集的目标检测数据集待续更新

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已于 2025-02-09 20:37:54 修改

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分类专栏：数据集文章标签：计算机视觉目标检测安全帽检测烟头检测数据集

于 2025-02-09 15:10:46 首次发布

本文链接：https://blog.youkuaiyun.com/matt45m/article/details/143523789

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3 篇文章

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1、鸟类检测

鸟类识别目标检测，只有bird一个类别，标注格式xml和txt两种，标注工具是labelImg,数据数量4849张，可用于yolo目标检测。
xml格式如下：

<annotation>
	<folder>bird1</folder>
	<filename>捕食的鸟_0.jpg</filename>
	<path>bird\捕食的鸟_0.jpg</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>1080</width>
		<height>1424</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>bird</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>397</xmin>
			<ymin>105</ymin>
			<xmax>871</xmax>
			<ymax>988</ymax>
		</bndbox>
	</object>
</annotation>

txt格式如下：

0 0.0693815987933635 0.3286445012787724 0.1297134238310709 0.18670076726342713
0 0.2450980392156863 0.592071611253197 0.1493212669683258 0.2225063938618926
0 0.11463046757164405 0.7161125319693096 0.1568627450980392 0.2915601023017903
0 0.07390648567119155 0.9079283887468031 0.10256410256410257 0.1790281329923274
0 0.40950226244343896 0.8542199488491049 0.15837104072398192 0.1534526854219949
0 0.5105580693815989 0.3900255754475704 0.16742081447963802 0.16112531969309465
0 0.7420814479638009 0.6662404092071612 0.18401206636500755 0.28900255754475707
0 0.9539969834087482 0.5549872122762148 0.07692307692307693 0.19437340153452687

在这里插入图片描述
数据集下载地址：https://download.youkuaiyun.com/download/matt45m/90358867?spm=1001.2014.3001.5503

2、无人机目标检测

无人机目标检测，只有无人机一个类别，标注格式xml，标注工具是labelImg,数据数量1097张，可以使用脚本把xml转成txt或者json格式的标签，可用于yolo目标检测。

<annotation>
	<folder>drone _ Google_da Ara</folder>
	<filename>pic_518.png</filename>
	<path>C:\Users\mehdi\Downloads\ss\drone _ Google_da Ara\pic_518.png</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>400</width>
		<height>300</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>64</xmin>
			<ymin>115</ymin>
			<xmax>200</xmax>
			<ymax>172</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>191</xmin>
			<ymin>129</ymin>
			<xmax>283</xmax>
			<ymax>166</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>188</xmin>
			<ymin>178</ymin>
			<xmax>334</xmax>
			<ymax>234</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>130</xmin>
			<ymin>248</ymin>
			<xmax>267</xmax>
			<ymax>292</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>88</xmin>
			<ymin>203</ymin>
			<xmax>170</xmax>
			<ymax>241</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>20</xmin>
			<ymin>193</ymin>
			<xmax>77</xmax>
			<ymax>237</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>137</xmin>
			<ymin>1</ymin>
			<xmax>288</xmax>
			<ymax>55</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>245</xmin>
			<ymin>105</ymin>
			<xmax>377</xmax>
			<ymax>165</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>319</xmin>
			<ymin>243</ymin>
			<xmax>399</xmax>
			<ymax>299</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>250</xmin>
			<ymin>272</ymin>
			<xmax>310</xmax>
			<ymax>300</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>1</xmin>
			<ymin>249</ymin>
			<xmax>83</xmax>
			<ymax>300</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>1</xmin>
			<ymin>45</ymin>
			<xmax>134</xmax>
			<ymax>116</ymax>
		</bndbox>
	</object>
	<object>
		<name>drone</name>
		<pose>Unspecified</pose>
		<truncated>1</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>307</xmin>
			<ymin>27</ymin>
			<xmax>400</xmax>
			<ymax>107</ymax>
		</bndbox>
	</object>
</annotation>

在这里插入图片描述
数据集地址：https://download.youkuaiyun.com/download/matt45m/90359135

3 、道路缺陷检测

道路缺陷检测，标注道路上有坑洼，标注格式xml，标注工具是labelImg,数据数量1097张，可以使用脚本把xml转成txt或者json格式的标签，可用于yolo目标检测，用于无人机道路检测维修。
xml数据格式如下：

<annotation>
	<folder>dataset</folder>
	<filename>img-411.jpg</filename>
	<path>/CSE-800/Thesis/2020-01-23/dataset/img-411.jpg</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>442</width>
		<height>300</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>201</xmin>
			<ymin>216</ymin>
			<xmax>299</xmax>
			<ymax>271</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>209</xmin>
			<ymin>137</ymin>
			<xmax>289</xmax>
			<ymax>165</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>252</xmin>
			<ymin>112</ymin>
			<xmax>316</xmax>
			<ymax>131</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>266</xmin>
			<ymin>94</ymin>
			<xmax>311</xmax>
			<ymax>106</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>212</xmin>
			<ymin>104</ymin>
			<xmax>268</xmax>
			<ymax>121</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>154</xmin>
			<ymin>122</ymin>
			<xmax>198</xmax>
			<ymax>135</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>157</xmin>
			<ymin>111</ymin>
			<xmax>193</xmax>
			<ymax>122</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>196</xmin>
			<ymin>97</ymin>
			<xmax>219</xmax>
			<ymax>104</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>244</xmin>
			<ymin>87</ymin>
			<xmax>277</xmax>
			<ymax>95</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>228</xmin>
			<ymin>80</ymin>
			<xmax>263</xmax>
			<ymax>88</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>26</xmin>
			<ymin>214</ymin>
			<xmax>70</xmax>
			<ymax>241</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>257</xmin>
			<ymin>69</ymin>
			<xmax>281</xmax>
			<ymax>76</ymax>
		</bndbox>
	</object>
	<object>
		<name>pothole</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>139</xmin>
			<ymin>100</ymin>
			<xmax>174</xmax>
			<ymax>110</ymax>
		</bndbox>
	</object>
</annotation>

在这里插入图片描述
数据集下载地址：https://download.youkuaiyun.com/download/matt45m/90359155

4、地上烟头检测

地上烟头目标检测，标注格式xml，标注工具是labelImg,数据数量1023张，可以使用脚本把xml转成txt或者json格式的标签，可用于深度学习计算机视觉目标检测，数据质量并不是很高。
标签统计结果：Bud: 1063

数据格式如下：

<annotation>
	<folder></folder>
	<filename>IMG_3231_png.rf.96f0f5fce3dfb25d09bb4f728b3e80ff.jpg</filename>
	<path>IMG_3231_png.rf.96f0f5fce3dfb25d09bb4f728b3e80ff.jpg</path>
	<source>
		<database>roboflow.ai</database>
	</source>
	<size>
		<width>1536</width>
		<height>2048</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>Bud</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<occluded>0</occluded>
		<bndbox>
			<xmin>366</xmin>
			<xmax>610</xmax>
			<ymin>827</ymin>
			<ymax>1096</ymax>
		</bndbox>
	</object>
	<object>
		<name>Bud</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<occluded>0</occluded>
		<bndbox>
			<xmin>513</xmin>
			<xmax>666</xmax>
			<ymin>771</ymin>
			<ymax>1037</ymax>
		</bndbox>
	</object>
	<object>
		<name>Bud</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<occluded>0</occluded>
		<bndbox>
			<xmin>739</xmin>
			<xmax>821</xmax>
			<ymin>476</ymin>
			<ymax>792</ymax>
		</bndbox>
	</object>
</annotation>

在这里插入图片描述
数据集下载地址：https://download.youkuaiyun.com/download/matt45m/90359300

5、骑车行人与安全帽检测

骑在车上的行人与行人佩戴安全帽检测，只标注骑在车上的人与安全头盔，标注格式xml，标注工具是labelImg,数据数量5448张，可以使用脚本把xml转成txt或者json格式的标签，可用于深度学习计算机视觉目标检测。
标注统计：

two_wheeler: 16759
helmet: 15348
without_helmet: 7876

数据格式：

<annotation>
		<filename>0005.jpg</filename>
		<object>
			<name>helmet</name>
			<ID>0</ID>
			<bndbox>
				<xmin>819</xmin>
				<ymin>672</ymin>
				<xmax>881</xmax>
				<ymax>738</ymax>
			</bndbox>
		</object>
		<object>
			<name>helmet</name>
			<ID>0</ID>
			<bndbox>
				<xmin>898</xmin>
				<ymin>682</ymin>
				<xmax>960</xmax>
				<ymax>748</ymax>
			</bndbox>
		</object>
		<object>
			<name>helmet</name>
			<ID>2</ID>
			<bndbox>
				<xmin>1460</xmin>
				<ymin>612</ymin>
				<xmax>1479</xmax>
				<ymax>632</ymax>
			</bndbox>
		</object>
		<object>
			<name>without_helmet</name>
			<ID>3</ID>
			<bndbox>
				<xmin>1566</xmin>
				<ymin>605</ymin>
				<xmax>1580</xmax>
				<ymax>619</ymax>
			</bndbox>
		</object>
		<object>
			<name>without_helmet</name>
			<ID>4</ID>
			<bndbox>
				<xmin>460</xmin>
				<ymin>680</ymin>
				<xmax>484</xmax>
				<ymax>707</ymax>
			</bndbox>
		</object>
		<object>
			<name>without_helmet</name>
			<ID>5</ID>
			<bndbox>
				<xmin>354</xmin>
				<ymin>686</ymin>
				<xmax>372</xmax>
				<ymax>720</ymax>
			</bndbox>
		</object>
		<object>
			<name>without_helmet</name>
			<ID>6</ID>
			<bndbox>
				<xmin>1235</xmin>
				<ymin>622</ymin>
				<xmax>1250</xmax>
				<ymax>642</ymax>
			</bndbox>
		</object>
		<object>
			<name>two_wheeler</name>
			<ID>3</ID>
			<bndbox>
				<xmin>1542</xmin>
				<ymin>601</ymin>
				<xmax>1605</xmax>
				<ymax>713</ymax>
			</bndbox>
		</object>
		<object>
			<name>two_wheeler</name>
			<ID>5</ID>
			<bndbox>
				<xmin>267</xmin>
				<ymin>685</ymin>
				<xmax>414</xmax>
				<ymax>886</ymax>
			</bndbox>
		</object>
		<object>
			<name>two_wheeler</name>
			<ID>4</ID>
			<bndbox>
				<xmin>376</xmin>
				<ymin>676</ymin>
				<xmax>556</xmax>
				<ymax>875</ymax>
			</bndbox>
		</object>
		<object>
			<name>two_wheeler</name>
			<ID>2</ID>
			<bndbox>
				<xmin>1443</xmin>
				<ymin>611</ymin>
				<xmax>1505</xmax>
				<ymax>714</ymax>
			</bndbox>
		</object>
		<object>
			<name>two_wheeler</name>
			<ID>6</ID>
			<bndbox>
				<xmin>1198</xmin>
				<ymin>616</ymin>
				<xmax>1286</xmax>
				<ymax>758</ymax>
			</bndbox>
		</object>
		<object>
			<name>two_wheeler</name>
			<ID>0</ID>
			<bndbox>
				<xmin>789</xmin>
				<ymin>670</ymin>
				<xmax>1095</xmax>
				<ymax>1071</ymax>
			</bndbox>
		</object>
	</annotation>

在这里插入图片描述
数据集下载地址，数据太大分两部分，部分一有完全的标签，部分2只有图像,part1下载地址：https://download.youkuaiyun.com/download/matt45m/90359276
部分2下载地址：https://download.youkuaiyun.com/download/matt45m/90359282

6、安全帽检测

施工现在行人与行人佩戴安全帽检测，标注了行人、没有带安全帽的人头、佩带安全帽的人头，标注格式txt，标注工具是labelImg,数据数量7492张，可用于深度学习计算机视觉目标检测，目标标签:person,head,helmet。
数据格式：

1 0.7019444444444445 0.16388657214345287 0.012777777777777779 0.020850708924103418
1 0.5152777777777777 0.6697247706422018 0.08388888888888889 0.15679733110925773
1 0.1388888888888889 0.9161801501251042 0.13777777777777778 0.16763969974979148
1 0.21083333333333334 0.5412844036697247 0.07388888888888889 0.1267723102585488
1 0.03888888888888889 0.46121768140116765 0.06222222222222222 0.08673894912427023
1 0.017777777777777778 0.40575479566305256 0.03333333333333333 0.0658882402001668
1 0.0225 0.33694745621351124 0.035 0.04670558798999166
1 0.011944444444444445 0.3653044203502919 0.02277777777777778 0.04670558798999166
1 0.07388888888888889 0.3427856547122602 0.03777777777777778 0.04837364470391994
1 0.04694444444444444 0.3010842368640534 0.021666666666666667 0.0316930775646372
1 0.135 0.3794829024186822 0.043333333333333335 0.058381984987489574
1 0.12666666666666668 0.34612176814011675 0.04111111111111111 0.05504587155963303
1 0.08944444444444444 0.30984153461217684 0.028888888888888888 0.03586321934945788
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在这里插入图片描述
数据集下载地址：https://download.youkuaiyun.com/download/matt45m/90359595

7、水面垃圾目标检测

水面垃圾目标检测，标注格式xml，标注工具是labelImg,数据数量2400张，可以使用脚本把xml转成txt或者json格式的标签，可用于深度学习计算机视觉目标检测。数据质量不是很高，是用几百张原始数据增强做成2000多张。
标注统计：
bottle: 1691
branch: 434
plastic-bag: 411
leaf: 267
milk-box: 255
plastic-garbage: 202
grass: 201
ball: 49

数据格式如下：

<annotation>
	<folder>JPEGImages</folder>
	<filename>001938.jpg</filename>
	<path>/home/zbr/VOCdevkit/VOC2007/JPEGImages/001938.jpg</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>416</width>
		<height>416</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>bottle</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>234</xmin>
			<ymin>244</ymin>
			<xmax>316</xmax>
			<ymax>283</ymax>
		</bndbox>
	</object>
	<object>
		<name>plastic-garbage</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>332</xmin>
			<ymin>172</ymin>
			<xmax>389</xmax>
			<ymax>226</ymax>
		</bndbox>
	</object>
	<object>
		<name>bottle</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>331</xmin>
			<ymin>315</ymin>
			<xmax>381</xmax>
			<ymax>363</ymax>
		</bndbox>
	</object>
	<object>
		<name>plastic-garbage</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>43</xmin>
			<ymin>191</ymin>
			<xmax>109</xmax>
			<ymax>220</ymax>
		</bndbox>
	</object>
</annotation>