Aiming to address the currently low accuracy of domestic industrial defect detection, this paper proposes a Two-Stage Industrial Defect Detection Framework based on Improved-YOLOv5 and Optimized-Inception-ResnetV2, which completes positioning and classification tasks through two specific models. In order to make the first-stage recognition more effective at locating insignificant small defects with high similarity on the steel surface, we improve YOLOv5 from the backbone network, the feature scales of the feature fusion layer, and the multiscale detection layer. In order to enable second-stage recognition to better extract defect features and achieve accurate classification, we embed the convolutional block attention module (CBAM) attention mechanism module into the Inception-ResnetV2 model, then optimize the network architecture and loss function of the accurate model. Based on the Pascal Visual Object Classes 2007 (VOC2007) dataset, the public dataset NEU-DET, and the optimized dataset Enriched-NEU-DET, we conducted multiple sets of comparative experiments on the Improved-YOLOv5 and Inception-ResnetV2. The testing results show that the improvement is obvious. In order to verify the superiority and adaptability of the two-stage framework, we first test based on the Enriched-NEU-DET dataset, and further use AUBO-i5 robot, Intel RealSense D435 camera, and other industrial steel equipment to build actual industrial scenes. In experiments, a two-stage framework achieves the best performance of 83.3% mean average precision (mAP), evaluated on the Enriched-NEU-DET dataset, and 91.0% on our built industrial defect environment.

For cylinder production, visual inspection of surface defects on cylinder joints holds great significance, yet the task is challenged by the high reflectivity on joint surfaces and the presence of numerous small defects. To address this issue, a metal surface defect detection network, named Information Augmentation and Multi-scale Feature fusion with YOLO (IAMF-YOLO) based on YOLOv8, is proposed in this paper, aiming to enhance the accuracy of joint surface defect detection. The IAMF-YOLO detection network incorporates an information augmentation strategy into the backbone network and introduces a novel backbone network called Information Augmentation Network (IAN). This IAN reduces the information loss during feature down-sampling extraction. Furthermore, to boost the detection performance of small defects on joint surfaces, improvements have been made to the Path Aggregation Network (PAN), and a Parallel Multi-scale Feature Pyramid Network (PMFPN) is introduced. PMFPN is designed to separate feature fusion into two stages: an initial feature fusion stage and an incremental feature fusion stage, of which the latter one further merges the features of the former one. Through ablation studies and comparative experiments on IAMF-YOLO, it is demonstrated that this model surpasses YOLOv8 in terms of detection accuracy and precision. Specifically, it achieves an accuracy of 88.6% on a self-constructed dataset, 91.0% on the NEU-DET dataset, and 84.2% on the GC10-DET dataset. Furthermore, this study demonstrates the robustness of the proposed model against environmental disturbances through actual defect detection under varying lighting conditions.

The orientation information is critical for the mobile robot. However, the robot’s constrained locomotion, complicated magnetic field environment, and the coupled orientation estimation present challenges for using the microelectromechanical magnetometer in real-time robot orientation determination. This article focuses on calibrating the magnetometer in constrained robot locomotion and achieving the robot’s decoupled roll, pitch, and yaw estimates. A magnitude-free and reference-free calibration approach is proposed to calibrate the magnetometer using the measurements collected from the principal plane of the robot locomotion. Moreover, a decoupled orientation estimator is created under the framework of the extended Kalman filter using the calibrated triaxial magnetic, angular rate, and gravity sensor readings. Two independent experiments validate the effectiveness of magnetometer calibration and orientation decoupling, respectively. A case study of estimating the mobile robot’s orientation shows that the proposed methods can achieve accurate heading estimation and decouple the roll and pitch from the magnetic field data.