Safety and security are critical issues in maritime environment. Automatic and reliable object detection based on multi-sensor data fusion is one of the efficient way for improving these issues in intelligent systems. In this paper, we propose an early fusion framework to achieve a robust object detection. The framework firstly utilizes a fusion strategy to combine both visible and infrared images and generates fused images. The resulting fused images are then processed by a simple dense convolutional neural network based detector, RetinaNet, to predict multiple 2D box hypotheses and the infrared confidences. To evaluate the proposed framework, we collected a real marine dataset using a sensor system onboard a vessel in the Finnish archipelago. This system is used for developing autonomous vessels, and records data in a range of operation and climatic and light conditions. The experimental results show that the proposed fusion framework able to identify the interest of objects surrounding the vessel substantially better compared with the baseline approaches.