Small target detection based on bird’s visual information processing mechanism


Detecting small targets in large fields of view is a challenging task. Nowadays, many targets detection models based on the convolutional neural network (CNN) achieve excellent performance. However, these CNN-based detectors are inefficient when applied to tasks of real-time detection of small targets. This paper proposes a small-target detection model in large fields of view based on the tectofugal–thalamofugal–accessory optic system of birds. Within this model, first, we design an unsupervised saliency algorithm to generate saliency regions to suppress background information according to the visual information processing mechanism of the tectofugal pathway of birds. Second, we design a super-resolution (SR) analysis method to enlarge small targets and improve image resolution by the information processing mechanism of the accessory optic system of birds. Then, according to the information processing mechanism of the thalamofugal pathway, we propose a CNN-based method to detect small targets. We further test our model on two public datasets (the VEDAI dataset and DLR 3 K dataset), and the experimental results demonstrate that the proposed detection model outperforms the state-of-the-art methods on small-target detection.

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This work was supported by the National Natural Science Foundation of China (NSFC) General program (61673353), Young Scientist Fund of NSFC (61603344) and Key research projects of Henan colleges and Universities(15A120017).

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Correspondence to Zhizhong Wang.

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Wang, Z., Liu, D., Lei, Y. et al. Small target detection based on bird’s visual information processing mechanism. Multimed Tools Appl 79, 22083–22105 (2020).

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  • Convolutional neural network
  • Small-target detection
  • Saliency algorithm
  • Super-resolution
  • Large fields of view