Keel bone lesions (KBLs) represent a relevant welfare concern in laying hens, arising from complex interactions among genetics, housing systems, and management practices. This study presents the development of an image analysis system for the automated detection and classification of KBLs in slaughterhouse videos, enabling scalable and retrospective welfare assessment. In addition to lesion classification, the system can track and count individual carcasses, providing estimates of the total number of specimens with and without significant lesions. Videos of brown laying hens from a commercial slaughterhouse in northeastern Italy were recorded on the processing line using a smartphone. Six hundred frames were extracted and annotated by three independent observers using a three-scale scoring system. A dataset was constructed by combining the original frames with crops centered on the keel area. To address class imbalance, samples of class 1 (damaged keel bones) were augmented by a factor of nine, compared to a factor of three for class 0 (no or mild lesion). A YOLO-based model was trained for both detection and classification tasks. The model achieved an F1 score of 0.85 and a mAP@0.5 of 0.892. A BoT-SORT tracker was evaluated against human annotations on a 5 min video, achieving an F1 score of 0.882 for the classification task. Potential improvements include increasing the number and variability of annotated images, refining annotation protocols, and enhancing model performance under varying slaughterhouse lighting and positioning conditions. The model could be applied in routine slaughter inspections to support welfare assessment in large populations of animals.