In the context of operating in low-light conditions where sufficient visual information is lacking, visual SLAM (Simultaneous Localization and Mapping) becomes a considerably challenging task. To address this issue, we propose a visual SLAM method based on Near-Infrared (NIR) illumination, which operates effectively in complete darkness while being visually friendly to the human eye. This approach employs NIR imagery to estimate the camera's motion and pose, achieving simultaneous localization and mapping. Through experiments and quantitative/qualitative analysis, the effectiveness of this method is demonstrated, particularly in low-light environments. Research findings also indicate that the performance of the NIR-based SLAM system is on par with its visible-light counterpart. Moreover, in indoor settings, the NIR-based SLAM system outperforms the visible-light SLAM system, suggesting that NIR-based SLAM could be a potential solution for robust camera pose estimation.
Defect inspection is indispensable process in manufacturing, and automatic optical inspection (AOI) has been rapidly applied to various areas. In AOI, artificial intelligence (AI) based deep learning methods are more and more advantageous in many fields. However, obtainment of high-performance deep learning algorithms always requires a large amount of training data, while defect samples are often scarce. So small sample has become one of the key problems in the industrial application of deep learning algorithms. Transfer learning enable us to utilize the knowledge of source domains to improve performance on target domain, which could be used to tackle the small sample problem intuitively. Therefore, this paper proposes a defect inspection network which is based on one of the transfer learning techniques: domain adaptation. We name the network as multi-source and multi-scale weighted domain adaptation network which is based on adversarial learning. Firstly, three adversarial domain adaptation modules are proposed to align feature distributions between multi-source domains and target domain under three scales, which make the backbone extract domain-invariant features. Simultaneously, the weights of domain adaptation module under each scale are set reasonably. Secondly, in order to reduce the effect of negative transfer, a novel similarity weight is proposed, which is applied on domain adaptation modules. Finally, experiments are carried out to prove the effectiveness of our method. The results show that our method can improve the mean average precision(mAP) from 62.3 to 78.5 in the case of 40 samples available for 4 defect categories, which surpasses other counterparts.
Fringe projection profilometry using a uniaxial MEMS micro-vibration mirror is becoming popular in three-dimensional (3D) reconstruction owing to the advantages of fast projection, small size, low cost, and no demand of focus optics. The calibration method is crucial and directly affects the accuracy of 3D reconstruction. In conventional phase-height calibration methods, there exists a problem of recalibration of system parameters if the maximum fringe frequency varies between the stages of calibration and reconstruction. In this paper, the fringe projection is realized by a MEMS mirror with a 1.15 kHz resonant frequency and a line laser. The voltage of line laser is modulated according to the scanning position, which is related to the vibration characteristics of MEMS mirror. Subsequently, the uniaxial MEMS-based 3D reconstruction system is constructed. We propose a novel calibration method for the uniaxial MEMS-based 3D reconstruction system. The proposed calibration method is derived from the scanning characteristics of a uniaxial vibration mirror and considers the camera distortion. The proposed method is free from the problem of recalibration and the limitations of installation. The experimental results show the proposed method can reconstruct the 3D shape of target in high resolution and verify the feasibility of the system.
Image super-resolution technology successfully overcomes the limitation of excessively large pixel size in infrared detectors and meets the increasing demand for high-resolution infrared image information. In this paper, the superresolution reconstruction of infrared images based on a convolutional neural network with a priori for high frequency information is reported. The main network structure is based on residual blocks, BN blocks that are not suitable for the super-resolution task are removed. The introduction of residual learning reduces computational complexity and accelerates network convergence. Multiple convolution layers and deconvolution layers respectively implement the extraction and restoration of the features in infrared images. images are divided into high frequency and low frequency parts. The low frequency part is the image of down-sampling, while the high frequency information is obeyed a simple case-agnostic distribution, which is equivalent to having a prior of high frequency information for the super-resolution network, Which is captures some knowledge on the lost information in the form of its distribution and embeds it into model’s parameters to mitigate the ill-posedness. Compared with the other previously proposed methods for infrared information restoration, our proposed method shows obvious advantages in the ability of high-resolution details acquisition.
Transfer learning is a promising method for AOI applications since it can significantly shorten sample collection time and improve efficiency in today’s smart manufacturing. However, related research enhanced the network models by applying TL without considering the domain similarity among datasets, the data long-tailedness of a source dataset, and mainly used linear transformations to mitigate the lack of samples. This research applies relational-based TL via domain similarity to improve the overall performance and data augmentation in both target and source domains to enrich the data quality and reduce the imbalance. Given a group of source datasets from similar industrial processes, we define which group is the most related to the target through the domain discrepancy score and the number of samples each has. Then, we transfer the chosen pre-trained backbone weights to train and fine-tune the target network. Our research suggests increases in the F1 score and the PR curve up to 20% compared with TL using benchmark datasets.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.