In the context of industrial manufacturing, quality control is a topic of primary importance due to its influence on multiple aspects of the production process. Ideally, an efficient and reliable quality control process in a mass production line ensures a steady throughput of goods that meet or exceed the production requirement parameters at each step of the manufacturing process, hence impacting significant aspects such as safety, durability, reliability and functionality of the product. This thesis, conducted within the framework of the MANAGE 5.0 (MANufacturing Automotive Green Evolution 5.0) project, investigates the application of state-of-the-art deep learning techniques in the context of the detection and localization of sealant application defects in next-generation car production lines.