Yining Wang

Aeroengine blades are critical components in engines, and any minor surface damage may lead to severe consequences. Therefore, high-precision damage segmentation techniques are crucial for accurately identifying blade damage. However, the high-precision localization of blade damages faces challenges due to the loss of detailed spatial features and the confusion of semantic features. In this paper, we propose a Spatial Contrast and Semantic Difference Perception Network (SSPN) for aeroengine blade damage segmentation, which effectively enhances the discriminative ability of spatial and semantic feature representations. Specifically, we propose a Class-aware Spatial Contrast (CSC) module to first generate segmentation masks and highlight categories. Then, CSC utilizes a class-aware contrastive learning method to enhance the distinguishability of spatial features. To establish clear semantic boundaries, we design a Semantic Difference Perception (SDP) module that first extracts semantic difference information between feature maps. Then, SDP uses a difference perception loss function to focus on low-quality areas and capture subtle semantic variances, which can produce a more balanced and contrasting semantic feature representation. We conduct comprehensive experiments to demonstrate the superiority of SSPN that achieves 93.42% mIoU on our blade dataset, surpassing existing state-of-the-art methods. Furthermore, we build an Aeroengine Blade Damage Analysis System to formulate more rational and economical maintenance strategies that can facilitate the development of the aviation engineering field.

The photovoltaic industry is a strategic industry with international competitive advantages and is developing toward a larger scale, higher efficiency, and higher quality. However, current researchers have not built a pixel-level defect inspection system for large-scale photovoltaic production processes. This article proposes an intelligent defect segmentation system combining the Internet of Things (IoT), artificial intelligence (AI), and edge computing for quality inspection of large-scale photovoltaic production lines. The intelligent factory based on this system is highly intelligent and deeply integrated, which can significantly reduce labor costs and improve factory productivity and product quality. The system’s core uses edge computing to segment cells in real-time by a lightweight defect segmentation model. Specifically, this article proposes a lightweight yet effective architecture named low-cost defect segmentation network (LDSN). An efficient split (ES) block is designed to support more channels and improve model accuracy without adding much computational complexity. Moreover, the ES block can express multiscale features in a finer granularity and enhance the information interaction between grouping features. In the decoding structure, a dual focus attention (DFA) that efficiently captures long-range spatial and channel information is proposed. Comprehensive experiments have been performed on a low-end PC with an NVIDIA GeForce RTX3060 GPU and an Intel Core i5-10600KF. LDSN-T-Lite achieves 84FPS and the F-measure optimal image scale (OIS) of 0.827, which only has 166K parameters and 395.6M memory usage on our PSCDE data set. A bigger version of LDSN-B achieves the F-measure OIS of 0.872, significantly outperforming current methods.