Accurate wound size measurement is essential for effective chronic wound management, guiding clinical decisions and predicting treatment efficacy, yet it remains a challenge due to the high time required by the manual measurement process and its subpar reproducibility. This study presents an automated detection, segmentation and measurement pipeline for chronic wounds using computer vision and deep learning. The impact of dataset composition on deep learning-based open wound detection and segmentation is investigated, along with the comparison of three object detection architectures (RetinaNet with MobileNetV2 backbone, and CenterNet with ResNetV1 or MobileNetV2) and two segmentation networks (DeepLabV3+ with ResNet50 backbone, and UPerNet with Swin Transformer). For wound measurement, traditional computer vision methods were employed to estimate the wound’s real-world width, height and area. Separate studies evaluated each task, followed by a complete pipeline assessment that couples the developed wound and reference marker detection model with the segmentation and measurement tasks. For wound and marker detection, the RetinaNet-MobileNetV2 achieved the best performance with mAP@.75IoU of 64.67% and 95.44%, respectively. The DeepLabV3-ResNet50 trained with all datasets achieved the best results in wound segmentation, with a Dice score of 89.83% following the complete pipeline, and a mean relative error of 16.09% for area estimation, surpassing the literature results. With the integration of the proposed pipeline in a smartphone application, this research aims to deliver a reliable wound measurement tool, empowering clinicians with accurate and objective data for improved treatment planning and enhanced patient outcomes in chronic wound care.