A large number of insulators play an important role in insulating and supporting complex power grids, and they are constantly exposed to challenges such as lightning strikes and contamination from the external environment. Only by accurately detecting insulator damage can potential safety hazards and equipment damage due to damaged insulators be avoided in time. Aiming at the problems of low accuracy and large model computation of existing insulator and defect detection algorithms, this paper proposes the insulator and defect detection model YOLO-S (YOLO-Small). First, the PAN structure in Neck uses lightweight convolutional GSConv instead of the standard convolutional Conv, introduces GSbottleneck on the basis of GSConv, and uses the method of OSA to design the cross-level partial network GSCSP module (VoV-GSCSP). The use of VoV-GSCSP instead of the C3 module in Neck reduces the computational effort and maintains the accuracy. Secondly, a new attention module, MaECA (MainECA), is designed based on the ECA attention mechanism to enhance target perception. After that, the SILU function in the SPPF in YOLOv5s is replaced with the Mish function, HardSwish function, and ReLU function, respectively, and the results show that the replacement of the Mish function (MishSPPF) is more effective in preventing the distortion of the image caused by image cropping and scaling, and thus improving the accuracy. Finally, the SIoU loss function is used to replace the original loss function CIoU, which improves the number of transmitted frames per second and the detection accuracy of the pictures.The mAP of YOLO-S is 4.2% higher than that of YOLOv5s, the detection accuracy is improved by 2.1%, and the model parameter computation is reduced by 6.0%, which still gives YOLO-S a higher recognition accuracy when compared with existing detection algorithms under the same conditions.

This article proposes a relative pose estimation method between non-cooperative spacecrafts based on parallel binocular vision. As the information of non-cooperative spacecraft in space is not accessible, the target is considered to be freely tumbling in space. The line feature of non-cooperative target is used to extract the feature points first; then the stereo matching and three-dimensional restructuring are taken for the feature points; finally, an algorithm based on parallel binocular vision algorithm is used to calculate the relative pose between the target coordinate and the world coordinate. The experimental results show that the proposed method has high-accuracy real-time performance.

Electric vehicle production is subjected to high manufacturing cost and environmental impact. Disassembling and remanufacturing the lithium-ion power packs can highly promote electric vehicle market penetration by procuring and regrouping reusable modules as stationary energy storage devices and cut life-cycle cost and environmental impact. Disassembly efficiency is crucial for battery remanufacturing companies in reverse supply chains. However, disassembly planning suffers from high computational complexity and inferior solutions. This paper developed a multi-objective mathematical model and presented a novel hybrid genetic-firework algorithm based on the precedence graph for obtaining solutions to disassemble the electric vehicle power pack into module levels in an efficient manner. The objectives for the model include not only smoothness of working stations, cycle time, and economic returns, but also consider operation safety and energy consumption. The proposed hybrid algorithm explored the performance of the novel solution searching mechanism of combining the firework and genetic algorithms. The proposed approach is compared with the commonly used multi-objective evolutionary algorithms in the literature, showing its feasibility and effectiveness.

The quality of underwater polarization imaging is mainly affected by the polarization properties of the target and impurity particles. Traditional methods often assume uniform polarization characteristics of the target, which make it difficult to address the restoration issues of complex targets. In response, a partition-based method for recovering underwater polarization images is proposed. The method involves pre-processing the image using the Gaussian curvature filtering algorithm, partitioning the image based on polarization information. In addition, a joint image evaluation method is used to achieve restoration of complex polarized characteristic targets. The method estimates the value of the reflected light polarization of one partition to estimate the value of the next partition and links the polarization values of each partition. Our approach achieves clear restoration results for multiple targets or complex structural objects underwater. Achieving significant improvement in image quality in multi-target underwater scenes, our method is highly effective for complex underwater environments. Experimental results show that our method, when compared with three other newer methods on multiple images of different targets and under varying scattering conditions, achieves an average increase of 617% in the standard deviation image evaluation index and a 61% optimization in the natural image quality evaluator index. Furthermore, our method is robust for different degrees of water turbidity.