In recent years, spiking neural networks, originating from computational neuroscience, have attracted widespread interest and attention in the fields of neuromorphic engineering and brain-inspired computing due to their unique characteristics. They have been explored for application in medical health, industrial inspection, intelligent driving, and other directions. Spiking neural networks possess rich spatiotemporal dynamic features that can simulate the pulse transmission and temporal coding mechanisms in the nervous system. Spiking neural networks operate more closely to the way biological neural systems function compared with traditional artificial neural networks. As one of the most cutting-edge intersections between neuroscience and artificial intelligence, research on spiking neural networks starts from the biological rationalization of neuron nodes and may further integrate brain-inspired insights, overcoming current limitations in energy consumption, robust stability, and continuous learning capabilities of artificial neural networks. This paper comprehensively introduces the classification and segmentation of static images based on spiking neural networks, target detection in dynamic images, as well as the latest advancements and implications of spiking neural networks. It discusses and analyzes the development opportunities and challenges in various key directions within the field of spiking neural networks.