In this work, a novel flexible electrically resistive-type MXene/Thermoplastic polyurethanes(TPU) based strain sensors was developed by a composite process of electrospinning (ES) and electrostatic spray deposition (ESD). Compared with other deposition processes, the sensing layer prepared by ESD has better adhesion to the ES TPU nanofiber membrane and is not easy to crack during the stretching process, thereby greatly improving the working range of the strain sensor. Furthermore, we obtained the sandwich structure easily by ES on the surface of the sensing layer again. This will help make the stress distribution more uniform during the stretching process and further increase the strain sensing range. The ESD-ES strain sensors were attached on skin to monitor various human motions. The results demonstrate that our ESD-ES strain sensors have wide application prospects in smart wearable device.

Infrared thermal imaging technology that has unique advantages in obtaining target information at night and in harsh weather conditions has been widely used in the military, security, environmental monitoring, and other fields. Existing visible light object detection methods often face challenges of high false alarm rates and low accuracy when detecting small infrared objects. Moreover, current small infrared object detection methods mostly require much detailed annotation data, and it is challenging to visually depict the positions of small targets on the original complex background images. To address these issues, we propose a multi-branch backbone and adaptive spatial feature fusion detection head-based YOLO network called MBFormer-YOLO to achieve the high-accuracy detection of small infrared targets. We design a multi-branch backbone with structural re-parameterization capability called MBFormer, which has strong capabilities to fit object features. In the neck part, MBFormer-YOLO utilizes convolutional block attention modules and C3-SwinTransformer modules to suppress noise and redundant information. We develop a 4-level adaptive spatial feature fusion detection head to improve the detection accuracy of the proposed network. Many experiments are conducted on the SIRST-V2 dataset to validate the effectiveness of MBFormer-YOLO. The results show that MBFormer-YOLO achieves an 11.3% increase in AP50 and 2% in AP50-95 over the baseline model YOLOv8n and surpasses other advanced object detection models.

The appearance defects in Multilayer Ceramic Capacitor (MLCC) adversely affect its performance and reliability. Thus, detecting these defects during MLCC production is imperative. However, this task faces numerous challenges, such as significant variations in the shape and size of defects, indistinct defect boundaries, and the inefficiency of manual detection methods. To address these issues, this paper proposes the RSE-YOLO model for identifying, localizing, and classifying defects in MLCC images. We design a novel backbone structure, namely the Residual Coordinate Weighted Convolutional Network, which possesses enhanced feature information extraction capabilities for accurately locating defect areas. Additionally, we introduce the Space Attention Pyramid Pooling Module to achieve weighted fusion of local and global feature information. Furthermore, the ECA-PAN is employed as the model’s neck structure to facilitate the fusion of feature information at different scales, improving the model’s generalization ability in multi-scale defect detection. Experimental results demonstrate that the RSE-YOLO model exhibits excellent performance on the MLCC dataset, achieving an mAP ₅₀ of 93.9%, mAP _50-95 of 63.2%, F1 of 90.9%, and a frame rate of 57 FPS, meeting the requirements for the task of MLCC appearance defect detection.

This paper proposes an improved defect detect algorithm named WGSO-YOLO for real-time detection of optical lens surface defects to solve the slow detection speed, low detection accuracy and unbalanced datasets commonly observed in optical lens surface defect detection. The proposed algorithm replaces the standard convolution with the GSConv module in the network feature fusion part, reducing the number of parameters and computational complexity without significant loss of contribution. It incorporates second-order channel attention mechanism to enhance the model’s feature extraction and fusion capabilities. In the prediction phase, the CIoU loss function is replaced with the WIoU loss function to improve the model’s generalization ability. The experiments demonstrate that our model performs exceptionally well on the optical lens surface defect dataset, achieving an FPS of 96 and an mAP@.5 of 0.927.