## 1 Introduction  
During gas-metal arc welding (GMAW), welding quality is influenced by various factors, including the welding conditions and external environment. Achieving a high welding quality requires careful monitoring, which is essential for verifying the planned welding process and assessing its quality. Various technologies have been utilized for the nondestructive inspection of GMAW, such as ultrasonic inspection, vision-based quality inspection, and process signal-based quality prediction techniques. A commonly applied method for vision-camera-based quality inspection involves measuring the external weld geometry and inspecting external defects using a laser vision sensor after welding. Specifically, it determines whether the quality standards required by the consumer are satisfied by extracting feature points after scanning the weld bead surface. However, this method has limitations in real-time monitoring and requires reworking when welding defects occur, leading to an increased working time. Another technique for determining the welding quality involves collecting electrical and acoustic information in real time during welding; however, it has limitations owing to its noise in the surrounding environment. The molten pool surface provides intuitive and abundant feature information that allows the welder to assess the welding quality and adjust the welding conditions during visual inspection [1]. Intelligent systems have been developed to improve the productivity of the welding process, and the acquisition of molten pool images utilizing vision sensorsis crucial for intelligent automated systems [2; 3; 4; 5; 6; 7; 8]. Although numerous studies have been conducted on vision monitoring systems for arc welding automation, they have primarily focused on gas tungsten arc welding, which has fewer limitations in acquiring molten pool images [9; 10; 11; 12; 13; 14; 15]. During GMAW, capturing images of the molten pool is challenging because of the dynamic vibrations caused by droplet transfer and volumetric changes in the arc plasma. In addition, the sufficiently strong radiation emitted from the arc plasma interrupts the observation of the region of interest (ROI), resulting in inevitable information loss. Radiation from the arc plasma is significantly stronger than that reflected and emitted from the molten pool surface or weld. The arc plasma is not an ROI, and more information can be obtained from the molten pool surface. Previous studies have divided gas-metal arc welding monitoring methods into active- and passive-vision methods. Active vision cuts off the arc plasma using physical methods, such as neutral density (ND) filters, and utilizes separate light sources to observe the molten pool surface and droplet transfer. Generally, a laser is utilized as the light source, and images are acquired using a high-speed camera. However, the dynamics of images in active vision is unsatisfactory despite the utilization of auxiliary light sources because ND filters with excessively high optical densities are used to inhibit the interference of arc plasma [16]. Active-vision system configurations incur considerable costs. In contrast, passive vision involves observing the molten pool by utilizing the emitted radiation with no auxiliary light source. Passive vision is suitable for intelligent welding because of its relatively simple configuration. Because vision systems focus more on applications in production lines than on research and analysis, a less costly method is reasonable. [PERSON] et al. [17] acquired molten pool images utilizing a 650 nm narrowband filter based only on the radiation from the molten pool during aluminum alloy gas tungsten arc welding. However, they neglected the quantum efficiency of the cameras. [PERSON] et al. [18] prepared a passive-vision sensor for molten pool observation and an active-vision sensor for weld bead geometry measurement and utilized a screen between the torch and laser module to avoid the interference of the radiation from the arc plasma.