Log diameter and volume measurements are crucial processes in the wood supply chain. Conventionally, the measurement mainly focuses on diameters and lengths. Despite the clear definition of conventional measurement methods, obtaining exact diameters is challenging due to the complexity of the measuring process and variability in the organic nature of logs. In this study, a smart handheld device, mainly composed of a single industrial camera, laser projectors, and a small embedded processor, is developed to capture and process log end images. Additionally, a measuring method is proposed, which consists of instance segmentation, contour fitting, and pose estimation, to automatically measure the diameters and estimate the volumes of cut-to-length (CTL) logs by using formulae. Specifically, instance segmentation is used to identify the salient object as a contour in the image space. Contour fitting is then applied to fit the contour into an ellipse, where the major and minor axes represent the long and short diameters of the object, respectively. Pose estimation is utilized to establish the relationship between the image space and physical space. Subsequently, several groups of comparative experiments are conducted with manual measurements as a reference. The experimental results indicate that, for log diameter measurement, approximately 97.33 % of the deviations between device measurement and conventional measurement can be expected to fall within a range of −0.54 cm and 0.61 cm, with mean absolute deviation (MAD) and mean relative deviation (MRD) of 0.25 cm and 0.78 %, respectively. In terms of volume estimations, MAD and MRD are found to be 0.23 and 0.56 %, respectively. The results imply that the device contributes to substantial improvements in accuracy, reliability, and traceability, and it shows promising potential to replace manual measurements.