BLEDNet: Bio-inspired lightweight neural network for edge detection

Abstract

Edge detection is fundamental to advanced computer vision tasks. Although deep learning-based methods can generate excellent results, they tend to be computationally expensive. To address this issue, researchers have explored the development of lightweight CNNs. Physiological studies have shown that there are two visual pathways in the biological visual system. The second visual pathway can modulate the first visual pathway, aiding the visual cortex in extracting edges from images more quickly. The inferior temporal cortex can integrate the edge signals extracted by the visual pathways and generate edges. Inspired by this, we designed a bio-inspired lightweight edge detection CNN, which includes an Encoder and a Decoder. The Encoder consists of a bio-inspired first visual pathway network and a bio-inspired second visual pathway network. The Decoder consists of a compact receptive field enhanced network. Specifically, the bio-inspired first visual pathway network can calculate the rate of change of image pixels. Through adaptive antagonism, signals with different rates of change are mutually suppressed. Meanwhile, the bio-inspired second visual pathway network can guide the bio-inspired first visual pathway network to focus more on the salient parts of the image. In addition, the receptive field-enhanced network can perceive image features distributed along specific directions, helping the decoder generate clearer edges. Our proposed method achieves competitive results on the BSDS500 dataset with ODS=0.805 and on the NYUD-v2 dataset with ODS = 0.757, while also having lower computational costs than most existing methods. Therefore, our approach is suitable for devices with lower computing capabilities.