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In view of the difficulty and low effectiveness of calorific value estimation while the straw fuel is sent to CFB boiler for combustion power generation, an image analysis system is designed to resolve the problem. The system consists of an industrial camera, an image analysis host, a server and an industrial interconnected communication network bus. The system uses industrial camera to gather images of straw fuel before entering the boiler, and the collected images are sent to the image analysis host through a high speed differential signal, the host uses the improved U-Net deep learning network to segment the image. The classification results are combined with the composition of the straw fuel and the calorific value obtained from the server, together with the water content of straw fuel returned in real time, then the calorific value of fuel can be calculated in real time based on caloric value formula. The test results show that the image segmentation algorithm based on improved U-Net deep learning network has nice segmentation effect. The mean Average Precision is over 0.86 and the mean Intersection over Union is over 0.68, which can meet the online estimation requirement of fuel calorific value.