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@inproceedings{Henrique2020Targetb,

title = {Convolutional Neural Networks for target detection in thermal images},

author = {Henrique Eduardo Macedo and Jos\'{e} Maria Parente Oliveira and Marcos Ricardo Omena Albuquerque M\'{a}ximo},

url = {https://www.sige.ita.br/edicoes-anteriores/2020/st/ST_03_3.pdf},

year  = {2020},

date = {2020-01-01},

booktitle = {Simp\'{o}sio de Aplica\c{c}\~{o}es Operacionais em \'{A}reas de Defesa 2020 (SIGE2020)},

abstract = {The use of thermal sensors embedded in remotely piloted aircraft constituted a remarkable advance in the oper- ational capacity of military forces. However, the amount of in- formation available alongside the regular workload overwhelms sensor operators. This study analyzes the performance of the YOLOv3 algorithm regarding target detection in a dataset of thermal images generated using the DcOMPASS sensor. The training method sought the configuration with the best performance conducting a hyperparameters search. Initially, training was carried out with the entire dataset, and then, separately with data only in blackhot or only whitehot. A software approach for the generation of images with inverted grayscale palette was also an option. The achieved results revealed training with images of opposite polarity (whitehot and blackhot) affects the final result negatively. The evaluation metrics were frame rate per second (FPS) and Mean Average Precision (mAP), and the finals scores demonstrate that YoloV3 can be successfully applied in the detection of targets in infrared images.},

keywords = {Convolutional Neural Network, Target detection},

pubstate = {published},

tppubtype = {inproceedings}

}