Abstract
The high dynamic range existing in arc welding with high energy density challenges most of the industrial cameras, causing badly exposed pixels in the captured images and bringing difficulty to the feature detection from internal weld pool. This paper proposes a novel monitoring method called adaptive image fusion, which increases the amount of information contained in the welding image and can be realized on the common industrial camera with low cost. It combines original images captured rapidly by the camera into one fused image and the setting of these images is based on the real time analysis of realistic scene irradiance during the welding. Experiments are carried out to find out the operating window for the adaptive image fusion method, providing the rules for getting a fused image with as much as information as possible. The comparison between the imaging with or without the proposed method proves that the fused image has a wider dynamic range and includes more useful features from the weld pool. The improvement is also verified by extracting both the internal and external features of weld pool within a same fused image with proposed method. The results show that the proposed method can adaptively expand the dynamic range of visual monitoring system with low cost, which benefits the feature extraction from the internal weld pool.
High-Dynamic Range, Night Vision, Image-Fusion Algorithm Based on a Decomposition Convolution Neural Network
