A feature aggregation network for contour detection inspired by complex cells properties.

Biological vision mechanism plays a very important role in detecting object contour information, so the research of bio-inspired contour detection has attracted widespread attention. In the visual physiological mechanism of contour detection, complex cells in the primary visual cortex (V1) receive the target edges output by simple cells, and subsequently aggregate and integrate the edges into contours. Inspired by the receptive field properties of complex cells in the V1 area, we design a contour detection network with an encoder-decoder structure, called the clustered suppression network. In the encoding network, we propose a feature aggregation encoding framework inspired by the clustered distribution properties of complex cells. At the same time, based on the inhibition mechanism properties of the non-classical receptive field of complex cells and the response properties to motion edges, we propose a suppression module and a spatial motion detection module, which are integrated into the encoding network. In the decoding network, we propose a highly aggregated decoding network inspired by the horizontal connection properties of complex cells. Choosing the BSDS500 natural scene dataset as the experimental object, the F-score is selected as the evaluation index. The average optimal <italic>F</italic>-score on a single-scale of the proposed method is 0.802. Concurrently, the NYUD-v2 dataset and BIPED dataset are used for further verification and achieved good results. The codes are available at https://github.com/smallsunq/CSNet-Contour-detection. [ABSTRACT FROM AUTHOR]