Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

Gaze estimation is a fundamental task in many applications of cognitive sciences, human–computer interaction, and robotics. The purely data-driven appearance-based gaze estimation methods may suffer from a lack of interpretability, which prevents their applicability to pervasive scenarios. In this study, a feature fusion method with multi-level information elements is proposed to improve the comprehensive performance of the appearance-based gaze estimation model. The multi-level feature extraction and expression are carried out from the originally captured images, and a multi-level information element matrix is established. A gaze conduction principle is formulated for reasonably fusing information elements from the established matrix. According to the gaze conduction principle along with the matrix, a multi-level information element fusion (MIEF) model for gaze estimation is proposed. Then, several input modes and network structures of the MIEF model are designed, and a series of grouping experiments are carried out on a small-scale sub-dataset. Furthermore, the optimized input modes and network structures of the MIEF model are selected for training and testing on the whole dataset to verify and compare model performance. Experimental results show that optimizing the feature combination in the input control module and fine-tuning the computational architecture in the feature extraction module can improve the performance of the gaze estimation model, which would enable the reduction of the model by incorporating the critical features and thus improve the performance and accessibility of the method. Compared with the reference baseline, the optimized model based on the proposed feature fusion method of multi-level information elements can achieve efficient training and improve the test accuracy in the verification experiment. The average error is 1.63 cm on phones on the GazeCapture dataset, which achieves comparable accuracy with state-of-the-art methods.

Optical performance evaluation is a critical process in the production of collimating lenses. However, the current visual inspection of lens light-spot images is inefficient and prone to fatigue. Intelligent detection based on machine vision and deep learning can improve evaluation efficiency and accuracy. In this study, a dual-branch structure light-spot evaluation model based on deep learning is proposed for collimating lens optical performance evaluation, and a lens light-spot image dataset is built, containing 9000 images with corresponding labels. Experimental results show that the proposed model achieves accurate classification of lens optical performance evaluation. Combined with the proposed weighted multi-model voting strategy, the performance of the model is further improved, and the classification accuracy successfully reaches 98.89%. Through the developed application software, the proposed model can be well applied to the quality inspection in collimating lens production.

Accurate pupil localization is a crucial requirement in gaze estimation, which can be employed to enhance the performance of human-computer interactions. Due to the complexity of practical scenarios, as well as the real-time and accuracy requirements of calculation, accurate pupil localization with robustness and real-time is still a challenging pattern recognition task. This study proposes a new pupil localization method based on Multi-directional Pixel Array Computation (MPAC), together with several optimization strategies including rotation transformation and pixel-array rotation similarity index. These strategies can further improve the localization accuracy by regulating pixel array distribution and fine-tuning the initial localization, respectively. For the application in gaze estimation, the proposed pupil localization method is encapsulated as a light-weight module and integrated into the gaze estimation model. To assess the accuracy and real-time performance of the proposed pupil localization method and compare it with the currently available methods, experiments on the BioID benchmark dataset were implemented. Furthermore, the training and test experiments of gaze estimation models were carried out to verify the contribution of the pupil localization module in improving the performance of gaze estimation. Experimental results show that the proposed method for robust and efficient pupil localization can be employed to achieve comparable performance with state-of-the-art learning-free methods. It has less computational complexity and achieves nearly real-time pupil localization with around 60 frames per second on the BioID benchmark dataset. Moreover, the proposed pupil localization method can be encapsulated as a callable module to improve the test accuracy of the gaze estimation models.

Transferring gaze estimation models from mobile terminals to in-vehicle driving scenarios faces challenges from differences in device form, viewpoint, and imaging conditions. To address this, this study proposes a cross-scene adaptation framework that integrates composite multi-stage transfer learning with distributed multi-camera fusion for personalized in-vehicle gaze estimation. The framework builds upon a multi-level information element fusion baseline (MIEF) and explores three transfer paths (CMP1–CMP3) for progressive personalized adaptation from a large-scale mobile source domain to an in-vehicle target scenario. A distributed multi-camera platform is established, producing gaze calibration data (GCD) for cross-session training and driving gaze and road scene data (DRD) for visualization validation. A Multi-Camera Fusion Model (MCF) is designed with two paradigms, MCF-Lite and MCF-Plus, to balance accuracy improvement and computational complexity. Experimental results demonstrate that the composite multi-stage transfer learning path CMP3 achieves an average error of 0.277 across camera views, with a best single-camera mean error of 0.244. Building on this, the distributed MCF-Plus model with the full C1 + C2 + C3 configuration further reduces the mean error to 0.160 and the standard deviation to 0.127, with 96.88% of predicted gaze points falling within the target calibration block (Error < 0.5). The proposed method provides a feasible technical pathway for personalized adaptation of gaze estimation models from mobile-terminal source domains to in-vehicle multi-camera target scenarios, and offers preliminary experimental evidence for gaze behavior perception and driving gaze heatmap generation in real environments.