The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, which is known as flickering characteristics. This phenomenon causes a severe decline in the detection performance of traditional detection methods. A biological memory model-based dynamic programming multi-target joint detection method was proposed to address this issue in this paper. Firstly, a global detection operator is used to discretize the multi-target state into multiple single-target states, achieving the discretization of numerous targets. Meanwhile, updating the formula of the memory weight merit function can strengthen the joint frame correlation of the flickering characteristics target. The progressive loop integral is utilized to update the target states to optimize the candidate target set. Finally, a two-stage threshold criterion is utilized to detect the target at different amplitude levels accurately. Simulation and experimental results are given to validate the assertion that the detection performance of the proposed method is greatly improved under a low SCR of 3-8 dB for multiple flickering target detection.
Pedestrian and cyclist detection based on deep neural network fast R-CNN