Comparison of Encoder-Decoder Networks for Soccer Field Segmentation | IEEE Conference Publication | IEEE Xplore

Comparison of Encoder-Decoder Networks for Soccer Field Segmentation


Abstract:

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...Show More

Abstract:

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.
Date of Conference: 09-11 October 2023
Date Added to IEEE Xplore: 05 December 2023
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Conference Location: Salvador, Brazil

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