Bipolar morphological YOLO network for object detection

Michael Zingerenko; Elena Limonova

doi:10.1117/12.3023255

3 April 2024 Bipolar morphological YOLO network for object detection

Michael Zingerenko, Elena Limonova

Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 130720Q (2024) https://doi.org/10.1117/12.3023255
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia

Abstract

There are various techniques for decreasing the computational complexity of neural networks, and a number of them use neuron approximations. A bipolar morphological neuron is an approximation of a classical neuron that can be used on FPGAs and ASICs to enhance computational efficiency. It uses 4 distinct computational pathways utilizing addition and maximum functions, in contrast to the traditional neuron which employs multiplication and addition. In this paper, we introduce bipolar morphological YOLO network for object detection task. To train the network, we employ an iterative approach that combines knowledge distillation for backbone and fine-tuning of the network’s head. Our experiments, which were conducted using the COCO dataset, yield results that are on par with classical networks. Specifically, the average recall for large images is 0.393 for the BM network and 0.371 for the classical network. Additionally, the average precision values are 0.088 for the BM network and 0.097 for the classical network. These outcomes establish a baseline for object detection using bipolar morphological networks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Michael Zingerenko and Elena Limonova "Bipolar morphological YOLO network for object detection", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 130720Q (3 April 2024); https://doi.org/10.1117/12.3023255

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