To address the challenges of low detection rate and high missed detection rate of military aircraft in current complex remote sensing data, and to meet the requirements of real-time detection and easy deployment of models, this paper introduces DET-YOLO, an innovative detection model. Firstly, to tackle the issue of reduced accuracy in identifying small targets amidst intricate backgrounds, a novel feature extraction component, C2f_DEF, was devised. This module replaced all existing C2f components within YOLOv8n, thereby significantly enhancing the model's ability to cope with complicated environmental contexts. Secondly, to achieve the functionality of easy deployment of the model, some deep structures were simplified to make the model more lightweight. Afterwards, to further improve the model's ability to handle complex backgrounds and dense environments in remote sensing images, and to improve the model's detection accuracy for military aircraft, the DAT module was embedded in the model. Finally, this paper also optimized the loss function and reg_max to further reduce computational costs while improving the detection accuracy of the model. To verify the effectiveness and strong universality of DET-YOLO, extensive experimental verification was conducted on three publicly available datasets, namely MAR20, NWPU VHR-10, and NEU-DET. On the MAR20 dataset, compared to other advanced models, DET-YOLO achieved the highest mAP0.5 (namely 94.7%) with only 80 training epochs, while meeting lightweight and real-time requirements. While on the other two datasets, DET-YOLO also achieved the best detection performance.

The method chosen to conduct vehicle dynamic modeling has a significant impact on the evaluation and optimization of ride comfort. This paper summarizes the current modeling methods of ride comfort and their limitations. Then, models based on nonlinear damping and equivalent damping and the multibody dynamic model are developed and simulated in Matlab/Simulink and Adams/Car. The driver seat responses from these models are compared, showing that the accuracy of the ride comfort model based on nonlinear damping is higher than the one based on equivalent damping. To improve the reliability of ride comfort optimization and analysis, a ride comfort optimization method based on nonlinear damping and intelligent algorithms is proposed. The sum of the frequency‐weighted RMS of the driver seat acceleration, the RMS of dynamic tyre load, and suspension working space is taken as the objective function in this article, using nonlinear damping coefficients and stiffness of suspension as design variables. By applying the particle swarm optimization (PSO), cuckoo search (CS), dividing rectangles (DIRECT), and genetic algorithm (GA), a set of optimal solutions are obtained. The method efficiency is verified through a comparison between frequency‐weighted RMS before and after optimization. Results show that the frequency‐weighted RMS of driver seat acceleration, RMS values of the suspension working space of the front and rear axles, and RMS values of the dynamic tyre load of front and rear wheels are decreased by an average of 27.4%, 21.6%, 25.0%, 19.3%, and 22.3%, respectively. The developed model is studied in a pilot commercial vehicle, and the results show that the optimization method proposed in this paper is more practical and features improvement over previous models.

In response to the low accuracy of traditional methods for detecting surface defects in lithium batteries, as well as the problems of large model size and high computational complexity in current detection models, this article proposes a new lightweight LF-YOLOv4 model that enhances image feature fusion. Firstly, replace the CSPDarknet53 backbone network in YOLOv4 with a lightweight MobileNetv2 network, thereby greatly reducing the computational parameters of the network while ensuring the ability to extract features. Secondly, in order to further reduce the number of model parameters and computational complexity, and minimize potential accuracy loss as much as possible, an improved depthwise separable convolution (DSC-SE-HsId) in this article was studied, which replaced some ordinary convolutions in the Neck and Head networks. Finally, in order to compensate for the partial accuracy loss caused by lightweight operations, and also to fuse feature maps of different scales to obtain more complete feature information, a new lightweight adaptive spatial feature fusion module (LSE-ASFF) in this article was studied and embedded behind the existing path aggregation network (PANet). In order to verify the performance and widespread applicability of the improved model, we conducted tests using the self-built lithium battery surface defect dataset, and the steel surface defect dataset provided by Northeastern University. Moreover, in order to prevent the network training from over fitting due to too few defect samples in the self-built dataset, we also used image enhancement to expand the dataset. And the experiment results show that the improved model proposed in this article achieves the highest TOPSIS score in both experimental datasets. Among them, compared with YOLOv4 on the self-built dataset, our improved model not only increases mAP₅₀ by 2.97%, reaching 97.83%, but also has model parameters of only 18.16% of the original model, floating point of operations of only 13.87% of the original model, model size of only 21.02% of the original model, and model training time shortened by 30.67% compared to the original model. At last, the effectiveness and superiority of the improved model in this article are demonstrated through example analysis and comparison.

To improve the detection accuracy of surface defects in dental nails in the current medical manufacturing industry, while reducing the model size and making it easy to deploy on resource limited devices, we propose a novel model structure for detecting surface defects on dental nails, SF-Yolov8n, based on Yolov8n. Its characteristic is that the model is not only lightweight, but has high detection performance. Firstly, to significantly reduce the number of parameters and volume of the model, while improving the detection accuracy of surface defects on small targets, we simplified the model structure by pruning some network feature layers and modules, and added an additional small target detection layer. Secondly, we developed a new lightweight module, C2Fast_CA, to replace some of the C2f modules in the model to reduce parameters. Afterwards, to further simplify the model structure and reduce computational complexity, we made exploratory adjustments to the reg_max in the model to find the minimum value that is most suitable for lightweight models, thereby achieving model miniaturization. Finally, we also optimized the loss function to improve the overall performance of the model in handling various difficult samples. The experimental results show that SF-Yolov8n performs better than other mainstream detection models in detecting surface defects of dental nails, and achieves the highest TOPSIS score. Besides, the parameter quantity of SF-Yolov8n is only 0.69M, which is a reduction of 77.01% compared to Yolov8n. Meanwhile, the P, R, and mAP ₅₀ of SF-Yolov8n are increased by 3.7%, 3.4%, and 5.8%, respectively, compared to Yolov8n.