Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector

In Industrie 4.0, machines are expected to become autonomous, self-aware and self-correcting. One important step in the area of manufacturing is feature recognition that aims to detect all the machining features from a 3D model. In this research area, recognising and locating a wide variety of highly intersecting features are extremely challenging as the topology information of features is substantially damaged because of the feature intersection. Motivated by the single shot multibox detector (SSD), this paper presents a novel deep learning approach named SsdNet to tackle the machining feature localisation and recognition problem. The typical SSD is designed for 2D image objection detection rather than 3D feature recognition. Therefore, the network architecture and output of SSD are modified to fulfil the purpose of this research. In addition, some advanced techniques are also utilised to further enhance the recognition performance. {Experimental results on the benchmark dataset confirm that the proposed method achieves the state-of-the-art feature recognition performance (95.20% F-score), localisation performance (90.62% F-score) and recognition efficiency (243.85 milliseconds per model).