Accurate and rapid detection of surface targets is a key technology for autonomous navigation of intelligent and unmanned ships. Faced with complex maritime environments and ever-changing maritime targets, it is impossible to consistently obtain accurate target detection results based on a single sensor. Infrared and visible light have strong complementarity. By fusing infrared and visible images, a more comprehensive and prominent fused image can be obtained, effectively improving the accuracy of target detection. This article constructs a lightweight convolutional neural network image fusion model based on the fusion framework of convolutional neural networks and then uses the constructed water surface dataset for comprehensive experimental testing of image fusion and object detection. The test results show that the object detection model trained using fused images has better detection performance than the object detection model trained using infrared and visible light images alone. So, integrating two types of images can provide better results for object detection and help promote the development of related technologies.