Machine vision techniques, including camera calibration methods, are of great importance for the development of vision-based measurements. However, in multi-camera calibration methods, rapidly constructing accurate geometric relationships among different coordinates is very difficult. Herein, we present a multi-camera calibration method capable of calibrating the intrinsic and extrinsic parameters of four cameras using only a single captured image per camera. Unlike Zhang’s method, which relies on multiple captured images to calibrate the cameras, the method uses a multi-plane stereo target containing multiple fixed planes to which coded patterns are attached. This target greatly reduces the time required for calibration and improves calibration robustness. The proposed method was experimentally compared with traditional camera calibration. The problem affecting the calibration accuracy in single calibration of multiple cameras is that the feature points on the captured images produce occlusion or different degrees of blurring; in the calibration of multiple cameras multiple times, the error accumulation caused by the calibration of two adjacent cameras is solved. This demonstration of a multi-camera calibration method improves camera calibration and provides a new design philosophy, to the best of our knowledge, for machine vision and vision-based measurement.

This paper presents an innovative seat suspension with variable stiffness and variable damping (VSVD) via magnetorheological fluid dampers to enhance ride comfort. The VSVD seat suspension is developed based on an innovative structure with springs in series and damping units, and its principles of VSVD and vibration attenuation are evaluated theoretically. The proposed VSVD seat suspension is then manufactured and tested. The test results illustrate that the equivalent stiffness of the seat suspension with power on ( I _s = 2.0 A current) increased to 186% and the equivalent damping ( I _d = 1.0 A) raised to 520% compared to the seat suspension with no current applied. The evaluation of vibration-attenuation capability of the seat suspension is conducted by employing Fuzzy logic controls and both harmonic and random signals are considered as vibration excitations. The experimental data show that the proposed seat suspension with controlled stiffness and damping results in lower seat acceleration compared to a passive seat or one with traditional damping control.

The compound eye of insects has many excellent characteristics. Directional navigation is one of the important features of compound eye, which is able to quickly and accurately determine the orientation of an objects. Therefore, bionic curved compound eye have great potential in detecting the orientation of the target. However, there is a serious non-linear relationship between the orientation of the target and the image obtained by the curved compound eye in wide field of view (FOV), and an effective model has not been established to detect the orientation of target. In this paper, a method for detecting the orientation of the target is proposed, which combines a virtual cylinder target with a neural network. To verify the feasibility of the method, a fiber-optic compound eye that is inspired by the structure of the bee’s compound eye and that fully utilizes the transmission characteristics and flexibility of optical fibers is developed. A verification experiment shows that the proposed method is able to realize quantitative detection of orientations using a prototype of the fiber-optic compound eye. The average errors between the ground truth and the predicted values of the horizontal and elevation angles of a target are 0.5951 ^° and 0.6748^°, respectively. This approach has great potential for target tracking, obstacle avoidance by unmanned aerial vehicles, and directional navigation control.

The defect detection of printed circuit board (PCB) images faces challenges such as limited sample number, imbalanced sample types, and varying detection reliability. To address these issues, this article proposes an uncertainty-aware unsupervised detection model on PCB images, short for $\rm { U^{2}D^{2}}$ PCB. The proposed method uses two U-Net networks to serve as the reconstructive subnetwork and the discriminative subnetwork, respectively. The former one reconstructs defect-free PCB images from defective PCB images, while the latter segments the defects and evaluates the defects uncertainty with the concatenated inputs of the defective and reconstructed images. The $\mathbf { U^{2}D^{2}}$ PCB model is trained in an unsupervised manner with only defect-free images embedding with multiscale artificial defects. Experimental results on the public PCB defect dataset and DeepPCB dataset demonstrate the effectiveness of the proposed method. The mean average precision (mAP) is 99.29% on the PCB defect dataset, while it reaches 95.78% on the DeepPCB dataset. These results are competitive to those of state-of-the-art (SOTA) fully supervised methods. The findings of $\mathrm { U^{2}D^{2}}$ PCB highlight the potential significance of using unsupervised learning techniques for PCB defect detection.

The limited load capacity and power resources of small-scale fixed-wing drones mean that it is difficult to employ internal high-precision inertial navigation devices to assist with the landing procedure. As an alternative, this paper proposes an attitude measurement system based on a monocular camera. The attitude angles are obtained from a single captured image containing five coded landmark points using the radial constraint method and three-dimensional coordinate transformations. The landing procedure is simulated for pitch angles from −15 ${}^{\circ }$ to −40 ${}^{\circ }$ , roll angles from −15 ${}^{\circ }$ to +15 ${}^{\circ }$ and yaw angles from −15 ${}^{\circ }$ to +15 ${}^{\circ }$ . For roll and pitch angles of approximately 0 ${}^{\circ }$ and −25 ${}^{\circ }$ , respectively, the accuracy of the method reaches 0.01 ${}^{\circ }$ and 0.04 ${}^{\circ }$ . This UAV attitude measurement system obtains an attitude angle by a single captured image, which has great potential for assisting with the landing of small-scale fixed-wing UAVs.