Abstract During abrasive flap wheel grinding, flaps may fail and tear prematurely. Such flap tears increase local tool flexibility and can impair the uniformity of material removal in automated grinding processes. To ensure process reliability, this paper proposes a novel computer vision approach for the automated detection and quantification of flap tears using deep learning. The processing pipeline is based on images showing the radial tool structure, followed by a geometric normalization that transforms the flap arrangement into a linear coordinate system. A YOLOv8-based object detection model is employed to localize abrasive flaps. To overcome class imbalances, a hybrid approach is implemented where the neural network performs a robust one-class detection, while the final distinction between intact flaps and stubs is achieved through deterministic, scale-invariant post-processing filters. The system was validated on a dataset including various tool diameters, grit sizes, and wear conditions. For tools within the trained distribution, the system achieved 96.5% classification accuracy and a Mean Absolute Error of 0.14 and 0.12 for flap and stub counts. However, performance is limited when encountering significant geometric domain shifts, such as larger tool diameters. The results demonstrate that the combination of deep learning and geometric heuristics provides a robust basis for automated flap tear detection in industrial flap wheel grinding applications. 1 Introduction Abrasive flap wheels are widely used for repair tasks or finishing of complex workpieces due to their high compliance [1, 2]. However, this compliance comes at the cost of high mechanical stress on the individual flaps, making tool wear a critical factor for process stability. In addition to grit wear and geometric change of the flap profile due to flap load, flaps may tear and detach prematurely. Ongoing investigations indicate that flap tears increase vibrations and localized loss of tool stiffness due to missing mutual support between