A Lightweight and Accurate Recognition Framework for Signs of X-ray Weld Images
This work addresses a domain-specific challenge in manufacturing quality inspection by improving recognition accuracy and speed for X-ray weld images, though it appears incremental as it builds on existing CNN methods.
The paper tackles the problem of recognizing signs in X-ray weld images, which is crucial for digital traceability in manufacturing, by proposing a lightweight CNN-based framework that achieves 99.7% accuracy in classification and 90.0 mAP in recognition with high efficiency.
X-ray images are commonly used to ensure the security of devices in quality inspection industry. The recognition of signs printed on X-ray weld images plays an essential role in digital traceability system of manufacturing industry. However, the scales of objects vary different greatly in weld images, and it hinders us to achieve satisfactory recognition. In this paper, we propose a signs recognition framework based on convolutional neural networks (CNNs) for weld images. The proposed framework firstly contains a shallow classification network for correcting the pose of images. Moreover, we present a novel spatial and channel enhancement (SCE) module to address the above scale problem. This module can integrate multi-scale features and adaptively assign weights for each feature source. Based on SCE module, a narrow network is designed for final weld information recognition. To enhance the practicability of our framework, we carefully design the architecture of framework with a few parameters and computations. Experimental results show that our framework achieves 99.7% accuracy with 1.1 giga floating-point of operations (GFLOPs) on classification stage, and 90.0 mean average precision (mAP) with 176.1 frames per second (FPS) on recognition stage.