Pallet racking is an essential element within warehouses, distribution centers, and manufacturing facilities. To guarantee its safe operation as well as stock protection and personnel safety, pallet racking requires continuous inspections and timely maintenance in the case of damage being discovered. Conventionally, a rack inspection is a manual quality inspection process completed by certified inspectors. The manual process results in operational down-time as well as 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 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%.

Rice is a widely consumed food across the world. Whilst the world recovers from COVID-19, food manufacturers are looking to enhance their quality inspection processes for satisfying exportation requirements and providing safety assurance to their clients. Rice cultivation is a significant process, the yield of which can be significantly impacted in an adverse manner due to plant disease. Yet, a large portion of rice cultivation takes place in developing countries with less stringent quality inspection protocols due to various reasons including cost of labor. To address this, we propose the development of lightweight convolutional neural network architecture for the automated detection of rice leaf smut and rice leaf blight. In doing so, this research addresses the issue of data scarcity via a practical variance modeling mechanism (Domain Feature Mapping) and a custom filter development mechanism assisted through a reference protocol for filter suppression.