提出了一种快速小目标检测方法。在算法优化方面,采用粒子群优化算法。为了克服传统粒子群优化算法的一些不足,对粒子群的拓扑结构进行了自适应的调整,改进了粒子群的多样性和寻优能力。在小目标检测方面,主要通过图像局部方差增量描述小目标作为图像局部灰度突变区域的这种特性。通过将粒子群优化算法引入到检测中,提高了检测速度。通过仿真实验,粒子群的寻优能力有了明显的增强,检测的性能有了大幅度的提升,并且检测结果是可靠和有效的。

An algorithm for small target detection with high speed was proposed. For optimization of algorithm, Particle Swarm Optimization (PSO) was utilized. In order to overcome some defects with PSO, a method about adaptive adjustment of swarm topology was developed, which improved the particles swarm diversity and strengthened their searching capabilities. For small target detection, the local variance increment can depict small target which acts as a sudden change with grayscale in local pixel space. PSO was combined with detection for decreasing the computation cost. Simulation shows that the improved PSO gets excellent search ability which accelerates detection, and the detection result is reliable and effective.