An inspection system comprising: one or more processors configured to obtain a first image of a work piece that has a fluorescent dye thereon in an ultraviolet light setting and a second image of the work piece in a visible light setting, the work piece being illuminated with an ultraviolet light in the UV light setting to cause the fluorescent dye to emit light and the work piece being illuminated with a visible light in the visible light setting to cause the work piece to reflect light, the first and second images being generated by one or more imaging devices in the same position relative to the work piece; wherein the one or more processors are configured to identify a candidate region of the first image based on a light characteristic of one or more pixels within the candidate region and to determine a corresponding candidate region of the second image that is at an analogous location as the candidate region of the first image, and wherein the one or more processors are configured to analyze both the candidate region of the first image and the corresponding candidate region of the second image to detect a potential defect on a surface of the work piece and a location of the potential defect relative to the surface of the work piece; wherein the one or more processors are configured to analyze the candidate regions (308, 314) of the first and second images (304, 302) by examining the candidate regions as inputs in a forward propagation direction through layers of artificial neurons in an artificial neural network, wherein the artificial neural network is a dual branch neural network having a first branch and a second branch, wherein the one or more processors input the candidate region of the first image into the first branch and input the candidate region of the second image into the second branch, and wherein the artificial neural network is configured to generate an output probability that the candidate regions (308, 314) of the first and second images (304, 302) depict a defect, and the one or more processors detect the potential defect in the surface of the work piece responsive to the output probability from the artificial neural network exceeding a designated probability threshold. one or more processors configured to obtain a first image of a work piece that has a fluorescent dye thereon in an ultraviolet light setting and a second image of the work piece in a visible light setting, the work piece being illuminated with an ultraviolet light in the UV light setting to cause the fluorescent dye to emit light and the work piece being illuminated with a visible light in the visible light setting to cause the work piece to reflect light, the first and second images being generated by one or more imaging devices in the same position relative to the work piece; wherein the one or more processors are configured to identify a candidate region of the first image based on a light characteristic of one or more pixels within the candidate region and to determine a corresponding candidate region of the second image that is at an analogous location as the candidate region of the first image, and wherein the one or more processors are configured to analyze both the candidate region of the first image and the corresponding candidate region of the second image to detect a potential defect on a surface of the work piece and a location of the potential defect relative to the surface of the work piece; wherein the one or more processors are configured to analyze the candidate regions (308, 314) of the first and second images (304, 302) by examining the candidate regions as inputs in a forward propagation direction through layers of artificial neurons in an artificial neural network, wherein the artificial neural network is a dual branch neural network having a first branch and a second branch, wherein the one or more processors input the candidate region of the first image into the first branch and input the candidate region of the second image into the second branch, and wherein the artificial neural network is configured to generate an output probability that the candidate regions (308, 314) of the first and second images (304, 302) depict a defect, and the one or more processors detect the potential defect in the surface of the work piece responsive to the output probability from the artificial neural network exceeding a designated probability threshold.