Sea-Surface Small Target Detection Using Spiking Neural Network With Controllable False Alarm

Convolutional neural network (CNN)-based detectors for small targets on the sea surface have proven effective, yet their increasingly complex structures and high energy demands pose challenges to deployment on resource-limited devices. To address this issue, this letter proposes a residual spiking network (RSN), which is developed within the advanced framework of spiking neural networks (SNNs). By using spiking neurons (SNs) as core computational units, the RSN can efficiently transmit features extracted from time-frequency graphs (TFGs) through discrete spikes in the backbone network. Meanwhile, the output is derived from the membrane voltage of the SNs, enabling reliable control over false alarms. Experimental results from the IPIX dataset validate the RSN’s stability in controlling the probability of false alarms (PFAs). With an observation time of 1.024 s and a PFA of 0.001, the average probability of detection (PD) is 0.8266. The RSN demonstrates a PD comparable to that of ResNet18 while consuming only 70% of its energy, highlighting its potential for practical applications in resource-constrained environments.