Biological edge detection for UCAV via improved artificial bee colony and visual attention

Purpose - The purpose of this paper is to propose a biological edge detection approach for aircraft such as unmanned combat air vehicle (UCAV), with the objective of making the UCAV recognize targets, especially in complex noisy environment. Design/methodology/approach - The hybrid model of saliency-based visual attention and artificial bee colony (ABC) algorithm is established for edge detection of UCAV. Visual attention can extract the region of interesting objects, and this approach can narrow the searching region for object segmentation, which can reduce the computational complexity. An improved ABC algorithm is applied in edge detection of the salient region. Findings - This work improved ABC algorithm by modifying the search strategy and adding some limits, so that it can be applied to edge detection problem. A hybrid model of saliency-based visual attention and ABC algorithm is developed. Experimental results demonstrated the feasibility and effectiveness of the proposed method: it can guarantee efficient target localization, with accurate edge detection in complex noisy environment. Practical implications - The biological edge detection model developed in this paper can be easily applied to practice and can steer the UCAV during target recognition, which will considerably increase the autonomy of the UCAV. Originality/value - A hybrid model of saliency-based visual attention and ABC algorithm is proposed for biological edge detection. An improved ABC algorithm is applied in edge detection of the salient region.