2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) 2018
DOI: 10.1109/aieee.2018.8592167
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Automated Image Annotation based on YOLOv3

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Cited by 16 publications
(10 citation statements)
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“…Another example of a convolutional neural network-based thermal image annotation is Reference [ 28 ], but the authors focus only on a single class and do not provide any number-based evaluation of their approach.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Another example of a convolutional neural network-based thermal image annotation is Reference [ 28 ], but the authors focus only on a single class and do not provide any number-based evaluation of their approach.…”
Section: Discussionmentioning
confidence: 99%
“…There are also propositions to use the networks pre-trained on RGB images to directly perform object detection on the thermal images [ 28 ], but this approach does not provide any benefit over the knowledge comprised in the neural network’s weights.…”
Section: Introductionmentioning
confidence: 99%
“…In order to better select the previous network, YOLOv3 inherits the YOLOv2 calculation anchor frame selection method and uses the K ‐means clustering method to train the bounding box. This method uses the IoU score as the final evaluation criterion and selects nine anchor points based on the average IoU to predict the bounding box thus achieving an improvement in precision [20]. The distance function formula used for clustering is dfalse(box,thinmathspacecentroidfalse)=1IoU)(box,thinmathspacecentroid…”
Section: Methodsmentioning
confidence: 99%
“…Then, CAN data from UDP capture is copied by a thread locking semaphore. To minimize annotation work, we used the pre-annotation approach [36] based on the TINYv3 [37] neural network. The pre-annotation detector was trained by 50,000 of images scaled down to 640 × 480 resolution taken from SCUT dataset with ''People-?''…”
Section: B Data Recordingmentioning
confidence: 99%