Comparison of Encoder-Decoder Networks for Soccer Field Segmentation - Guimarães, Otávio H. R.; Maximo, Marcos R. O. A.; Oliveira, Jose Maria Parente de (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS); 2023: Anais do XV Simpósio Brasileiro de Robótica e XX Simpósio Latino Americano de Robótica, 2023)
Convolutional neural networks consist of state-of the-art models used for the solution of computer vision problems. This paper contributes by evaluating the efficiency of several encoder-decoder neural networks, trained to perform the segmentation of the soccer field in Humanoid KidSize Robot Soccer competitions. To compare the efficiency of several encoders, a total of fourteen neural network models, based on the U-Net and SegNet architectures, were tested and compared in terms of accuracy, cost function value, IoU, and average inference time. Based on that, the networks based on U-Net that utilized the MobileNetv3Small or the ResNet18 for the encoding process were found to be the optimal solution among the considered alternatives to segment the soccer field.