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cef4dba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 | <!DOCTYPE html>
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<title>AI Road Hazard Detection</title>
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<!-- Slide 7: Dataset -->
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<h2 class="text-4xl font-bold text-center mb-16 text-gray-800">Our Dataset</h2>
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<h3 class="text-2xl font-bold text-gray-800">Public Dataset</h3>
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<p class="text-gray-600 mb-4">
We started with publicly available road datasets containing labeled images of potholes and speed bumps from various sources.
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<ul class="space-y-2 text-gray-700">
<li class="flex items-start">
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<span>10,000+ images from open repositories</span>
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<span>Various lighting and weather conditions</span>
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<li class="flex items-start">
<i class="fas fa-check-circle text-blue-500 mt-1 mr-2"></i>
<span>Multiple road types (urban, rural, highways)</span>
</li>
</ul>
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<h3 class="text-2xl font-bold text-gray-800">Our Custom Dataset</h3>
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<p class="text-gray-600 mb-4">
Each team member contributed by collecting and labeling images from their local areas to enhance diversity.
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<ul class="space-y-2 text-gray-700">
<li class="flex items-start">
<i class="fas fa-check-circle text-purple-500 mt-1 mr-2"></i>
<span>5,000+ new images collected locally</span>
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<li class="flex items-start">
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<span>Each town's unique road conditions captured</span>
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<li class="flex items-start">
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<span>Seasonal variations included</span>
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</ul>
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<div class="bg-gray-50 rounded-xl shadow-lg p-8 mb-10">
<h3 class="text-2xl font-bold text-center mb-6 text-gray-800">Annotation Process</h3>
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<div class="bg-blue-100 w-16 h-16 mx-auto rounded-full flex items-center justify-center mb-3">
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<h4 class="font-semibold mb-2">Roboflow</h4>
<p class="text-sm text-gray-600">Used for consistent labeling across all images</p>
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<h4 class="font-semibold mb-2">Bounding Boxes</h4>
<p class="text-sm text-gray-600">Precise annotation of potholes and speed bumps</p>
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<h4 class="font-semibold mb-2">Augmentation</h4>
<p class="text-sm text-gray-600">Applied transformations to increase diversity</p>
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</div>
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<div class="md:w-1/2 p-8">
<h3 class="text-2xl font-bold mb-4 text-gray-800">Final Merged Dataset</h3>
<p class="text-gray-600 mb-6">
After careful processing and validation, we combined both datasets to create a robust training set.
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<h4 class="font-semibold">Dataset Statistics</h4>
<ul class="text-sm text-gray-600 mt-1 space-y-1">
<li>15,000+ total images</li>
<li>25,000+ labeled potholes</li>
<li>8,000+ labeled speed bumps</li>
<li>Class balance maintained</li>
</ul>
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<h4 class="font-semibold">Split</h4>
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<li>70% training set</li>
<li>20% validation set</li>
<li>10% test set</li>
</ul>
</div>
</div>
</div>
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<img src="https://miro.medium.com/v2/resize:fit:1400/1*Q3TGVXm5W0V5yG5y5y5y5Q.png"
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