Pallet recognition and positioning is one of the critical problems in unmanned forklift trucks. At present, target detection is mostly used for pallet positioning. However, target detection can only recognize the position of the pallet in the image and cannot obtain the spatial information of the pallet. To solve this problem, this study proposes a pallet positioning method based on target and key point detection with monocular vision, which is applied to detect the pallet and calculate the current dip angle and distance of the pallet. Firstly, target detection is carried out on the pallet. Then, the image will be cropped according to the detection result and input into the key points detection network. Through the detection of the key points and the inherent geometric features of the pallet, the edge adaptive adjustment is designed to obtain the high-precision profile information of the pallet. According to the geometric constraints, a method for calculating the dip angle and distance of the pallet based on contour points is proposed, and the RANSAC algorithm is adopted to improve the precision and stability of the calculation results, thus addressing the problem of pallet positioning. Experiments indicate that the average error of the proposed algorithm is less than 5° in the calculation of dip angle and less than 110 mm in the calculation of horizontal distance. It works well for pallet positioning and is of high practical value.