OTNet: A Small Object Detection Algorithm for Video Inspired by Avian Visual System

Pingge Hu, Xingtong Wang, Xiaoteng Zhang, Yueyang Cang and Li Shi ()
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Pingge Hu: Department of Automation, Tsinghua University, Beijing 100084, China
Xingtong Wang: Department of Automation, Tsinghua University, Beijing 100084, China
Xiaoteng Zhang: Department of Automation, Tsinghua University, Beijing 100084, China
Yueyang Cang: Department of Automation, Tsinghua University, Beijing 100084, China
Li Shi: Department of Automation, Tsinghua University, Beijing 100084, China

Mathematics, 2022, vol. 10, issue 21, 1-18

Abstract: Small object detection is one of the most challenging and non-negligible fields in computer vision. Inspired by the location–focus–identification process of the avian visual system, we present our location-focused small-object-detection algorithm for video or image sequence, OTNet. The model contains three modules corresponding to the forms of saliency, which drive the strongest response of OT to calculate the saliency map. The three modules are responsible for temporal–spatial feature extraction, spatial feature extraction and memory matching, respectively. We tested our model on the AU-AIR dataset and achieved up to 97.95% recall rate, 85.73% precision rate and 89.94 F 1 score with a lower computational complexity. Our model is also able to work as a plugin module for other object detection models to improve their performance in bird-view images, especially for detecting smaller objects. We managed to improve the detection performance by up to 40.01%. The results show that our model performs well on the common metrics on detection, while simulating visual information processing for object localization of the avian brain.

Keywords: small object detection; bio-inspired algorithm; avian visual system; neural network; deep learning; optic tectum (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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