Bonnet polishing is widely used for processing aspherical optical components with nanometer surface roughness and submicrometer shape accuracy. The traditional bonnet tool wear detection method is expensive, time consuming, and has low efficiency. This study proposes a wear detection method for bonnet tools based on a splicing data acquisition platform and an improved iterative closest point (ICP) splicing algorithm. This method calculates the extent of wear in large bonnet tools using point cloud splicing and a bonnet wear detection algorithm. The point cloud preprocessing for mosaic data is conducted using voxel down sampling and radius filtering. A good initial registration transformation matrix is obtained using the splicing detection data acquisition platform. Finally, point cloud precise registration is realized using the bidirectional K-D tree nearest neighbor search combined with the ICP algorithm. Experimental results demonstrate that the stitching algorithm proposed herein can greatly enhance registration efficiency while ensuring registration accuracy. Moreover, it does not affect the accuracy of subsequent bonnet wear detection, which guarantees wear detection of large bonnet tools.