Aviation-engine blade surface anomaly detection based on the deformable neural network

The abstract serves both as a general introduction to the topic and as a brief, non-technical summary of the main results and their implications. Authors are advised to check the author instructions for the journal they are submitting to for word limits and if structural elements like subheadings, citations, or equations are permitted. Engine blades, as key components of the aviation engine, operate under high-speed rotation and high-temperature conditions, making them susceptible to defects such as fatigue, cracks and corrosion. This paper presents an innovative approach to detecting defects in aviation engine blades. To increase the detection accuracy of irregular defects, we design a novel deformable convolutional network (DCN) based feature extraction module. It employs the deformable convolutional structure to extract the features of blades with different shapes. To enhance the accuracy of locating small targets, the Channel Attention Module is adopted to enable the network focus on surface anomalies. Apart from that, the DSConv module is designed to decrease the model parameters and improve the detection speed. Extensive tests have been conducted on the collected dataset of aviation engine blade with surface defects. The algorithm achieves an average detection accuracy of 97.1%. The algorithms inference performance could reach up to 25 fps on the TX2 device, which satisfies the real-time detection requirement.