Vehicle Logo Recognition with Small Sample Problem in Complex Scene Based on Data Augmentation

<div>Results of original experiment. We can see that the loss value is stable after 20000 iterations. (a) Loss curve. (b) Curve of <i>R</i> value, <svg height="8.68572pt" id="M69" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.15071 8.68572" width="8.15071pt" 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="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"></path></g></svg> value, and <svg height="8.68572pt" id="M70" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 14.2836 8.68572" width="14.2836pt" 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="M584 650H137L131 622C214 614 217 612 200 521L125 127C109 41 101 35 23 28L17 0H288L294 28C201 35 193 42 209 128L242 309H348C440 309 442 300 443 226H471L510 422H482C452 354 449 348 357 348H251L295 575C302 609 304 615 338 615H426C502 615 517 604 526 581C534 560 536 524 537 492L565 494C574 554 583 631 584 650Z"></path></g><g transform="matrix(.013,0,0,-0.013,7.895,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"></path></g></svg> value.</div>

Mathematical Problems in Engineering

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Figure 5

Figure 5: Vehicle Logo Recognition with Small Sample Problem in Complex Scene Based on Data Augmentation 