YOLO-ASC: You Only Look Once And See Contours

YOLO is a useful, one-stage tool for object detection and classification. In this paper, we consider the application of grocery product detection. The grocery stores have a significant amount of product classes, so it is beneficial to postpone the classification into a second, specialized neural network with a higher capacity. Extracting bounding boxes for a classification network is not precise enough as the detected area includes redundant information about the background. We propose YOLO-ASC, which, for rectangular-based objects, detects bounding boxes together with object contour using a quadrangular. This approach allows detecting objects more accurately and without the background. For the quadrangular detection functionality, YOLO-ASC shares the feature maps that are already present in the network, and therefore its inference time is almost identical to the original YOLO. YOLO reaches high detection precision by using YOLO apriori knowledge, anchors extracted from data. In this work, we present two experiments where we demonstrate that YOLO-ASC training converges faster due to the symbiosis between the bounding box detection and quadrangular detection. Finally, we propose a tool for generating synthetic datasets with quadrangular labels that is helpful for transfer learning.