Computer vision based cargo boxes pose adjustment system for two-dimensional conveyor platform

For enhancing the accuracy of cargo box position and angle recognition on the conveyor platform, this paper proposes a cargo box attitude detection and adjustment method based on instance segmentation and image processing. This approach involves generating Mask data through target detection of the cargo box using Mask R-CNN. Using image processing algorithms to generate a minimum rectangle according to the Mask data, and the minimum rectangle data is aligned with the Bbox data of Maks R-CNN. The position and angle of the cargo box are detected based on the minimum rectangular data, and the conveyor platform is adjusted to control the cargo box attitude using the Bbox data. Nine attitude acquisition and comparison experiments were conducted on the cargo box using an angle sensor, and the deviation of the method was consistently \(<0.6^{\circ }\) $<0.6^{\circ }$ , with a relative error of 1.27% for the nine total changes. Importantly, the image processing technique in this study avoids external image processing, reducing ambient light impact and enhancing cargo box recognition and attitude feedback functionality on the conveyor platform. Throughout the entire cargo box adjustment experiment, the cargo box’s can be stabilized to reach the set angle.