Research on Deviation Detection of Belt Conveyor Based on Inspection Robot and Deep Learning

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="11"><hr/></td></tr><tr class="thead"><td class="align_center" colspan="11">Parameter</td></tr><tr class="thead"><td class="align_center" rowspan="2">Height (m)</td><td class="align_center" rowspan="2"><svg height="11.8174pt" id="M223" style="vertical-align:-3.1815pt" version="1.1" viewbox="-0.0498162 -8.6359 23.6392 11.8174" width="23.6392pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 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243 122 209 182 182C232 159 261 135 261 91C261 48 227 21 190 21C130 21 85 79 68 145L41 140C41 104 51 36 58 22C75 7 121 -12 172 -12C252 -12 337 35 337 126C337 195 286 231 210 262C166 281 126 304 126 348C126 388 152 417 191 417C240 417 274 378 294 318L319 325Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,11.913,3.132)"><path d="M298 36L289 62C276 55 253 45 228 45C202 45 169 60 169 141V397H276C289 405 292 426 282 437H169V574L155 576L90 509V437H45L17 408L21 397H90V107C90 28 125 -12 188 -12C198 -12 213 -8 230 1L298 36Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,14.634,3.132)"><path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,18.501,3.132)"><path 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527H259L583 -8H612L690 390C708 481 720 535 728 558C744 603 756 619 816 622L822 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.062,3.132)"><path d="M387 400C387 425 348 451 303 451C247 451 176 414 132 376C69 322 24 228 24 148C24 43 74 -12 147 -12C211 -12 301 33 363 103L346 128C319 99 249 51 193 51C148 51 112 84 112 165C112 230 130 287 154 330C170 359 199 400 243 400C277 400 304 383 326 354C333 345 343 343 354 348C378 360 387 382 387 400Z"></path></g></svg></span></td></tr><tr><td class="thead-hr" colspan="11"><hr/></td></tr><tr><td class="align_left">2.4</td><td class="align_center">41</td><td class="align_center">1</td><td class="align_center">8</td><td class="align_center">0.35</td><td class="align_center">0.05</td><td class="align_center">4</td><td class="align_center">−0.5</td><td class="align_center">0.5</td><td class="align_center">0.05</td><td class="align_center">4</td></tr><tr><td class="align_left">2.6</td><td class="align_center">37</td><td class="align_center">1</td><td class="align_center">8</td><td class="align_center">0.25</td><td class="align_center">0.05</td><td class="align_center">4</td><td class="align_center">−0.5</td><td class="align_center">0.5</td><td class="align_center">0.05</td><td class="align_center">4</td></tr><tr><td class="align_left">2.8</td><td class="align_center">36</td><td class="align_center">1</td><td class="align_center">8</td><td class="align_center">0.2</td><td class="align_center">0.05</td><td class="align_center">4</td><td class="align_center">−0.5</td><td class="align_center">0.5</td><td class="align_center">0.05</td><td class="align_center">4</td></tr><tr><td class="align_left">3.0</td><td class="align_center">34</td><td class="align_center">1</td><td class="align_center">8</td><td class="align_center">0.2</td><td class="align_center">0.05</td><td class="align_center">4</td><td class="align_center">−0.5</td><td class="align_center">0.5</td><td class="align_center">0.05</td><td class="align_center">4</td></tr><tr class="table-tr"><td colspan="11"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>The prior transformation parameters for the outer idler outer edge detection.</div>

Complexity

tab4

Table 4

Table 4: Research on Deviation Detection of Belt Conveyor Based on Inspection Robot and Deep Learning 