Target detection approach for UAVs via improved Pigeon-inspired Optimization and Edge Potential Function

In this paper, the hybrid model of Edge Potential Function (EPF) and Simulated Annealing Pigeon-inspired Optimization (SAPIO) algorithm is proposed to accomplish the target detection task for Unmanned Aerial Vehicles (UAVs) at low altitude. EPF can be calculated from the edge map of the original image and provide a kind of attractive pattern for the given target, which is conventionally exploited by the optimization algorithms. Pigeon-inspired Optimization (PIO) is a novel bio-inspired computation algorithm, which was inspired from the homing characteristics of pigeons. In this paper, the simulated annealing mechanism is adopted in our SAPIO algorithm for maximizing the value of EPF. A series of comparative experiments with standard Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC) and PIO algorithms demonstrate the robustness and effectiveness of our SAPIO algorithm. Meanwhile, the proposed approach can guarantee accurate target matching.