Pallet racking is an essential element within warehouses, distribution centers and manufacturing facilities. To guarantee its safe operation, stock protection and personnel safety, pallet racking requires continuous inspection and timely maintenance in the case of damage being discovered. Conventionally, rack inspection is a manual quality inspection process, completed by certified inspectors. The manual process results in operational down-time, inspection and certification costs and undiscovered damage due to human error. Inspired by the trend toward smart industrial operations, we present a computer vision based autonomous rack inspection framework centered around the Yolov7 architecture. Additionally, we propose a domain variance modeling mechanism for addressing the issue of data scarcity through the generation of representative data samples. Our proposed framework achieved a Mean Average Precision of 91.1%.