Abstract: Forest mobile robots based on visual navigation face the problem of limited computational power as edge devices and the navigation performance is greatly affected by illumination. To address this, a lightweight trunk detection method is proposed. This method uses visible and thermal image as inputs, minimizing the impact of illumination on navigation performance it also employs a feature extraction module based on Partial Convolution（PConv） and a Partial Efficient Layer Aggregation Network（P-ELAN） to achieve lightweight improvements to the baseline model. During training, the alpha-CioU loss function is used to replace the original CIoU loss function, increasing the accuracy of bounding box regression. The results show that the proposed tree trunk detection method for forest mobile robots reduces the parameter count of the original YOLOv7-tiny model by 31. 7%, decreases computation by 33. 3%, and improves inference speeds on Graphics Processing Units（GPU） and Central Processing Units（CPU） by 33. 3% and 7. 8%. The modified model maintains comparable accuracy while being more lightweight, making it an ideal choice for deployment on edge devices such as robots.