Utilizing Unmanned Aerial Vehicles (UAV) and instance segmentation for construction site monitoring(such as construction machinery and operation surfaces) offers a significant leap in management efficiency over traditional manual supervision methods. However, in UAV-based remote sensing images, the subtle presence of construction machinery and the image features resemblances among various operational surfaces make it difficult to segment instances. To address these challenges, this study proposed a novel instance segmentation model based on the YOLOv8-seg model. Given the unique challenges, the proposed model makes three improvements to the original YOLOv8-seg model. First, the paper incorporates the FocalNext module, which extends the sense field of the convolutional kernel to capture contextual data and integrates multilevel features, enhancing the perception of local details. Second, the paper incorporates the Efficient Multiscale Attention (EMA) module, which refines image features by emphasizing spatial-channel interactions and adeptly contrasts patterns across scales to detect nuances overlooked by conventional models, aiding in distinguishing similar construction operation surfaces. Last, given the intricate nature of construction site images, this paper incorporates the Context Aggregation module, which enhances pixel analysis by intelligently modulating feature weights to highlight essential global contexts. The ablation experiment demonstrates that the enhancements perform well on the YOLOv8-seg two variants model. Comparative experimental results show that the improved model significantly outperforms existing instance segmentation models regarding model performance, complexity, and inference speed. Overall, the improved YOLOv8-seg model balances model performance and computational complexity to meet the needs of edge device deployment in field monitoring.

Object detection in remote sensing imagery exhibits difficulties due to complex backgrounds, diverse object scales, and intricate spatial context relationships. Motivated by the problems mentioned above, this paper introduces AeroDetectNet, a novel lightweight and high-precision object detection network custom-designed for aerial remote sensing scenarios, building upon the YOLOv7-tiny algorithm. It enhances performance through four key improvements: the normalized Wasserstein distance for consistent object size sensitivity, the Involution module for reduced background noise, a self-designed RCS-biformer module for better spatial context interpretation, and a self-designed WF-CoT SPPCSP feature pyramid for improved feature map weighting and context capture. Ablation studies conducted on a hybrid dataset composed of three open-source remote sensing datasets (including NWPU VHR-10 remote sensing images, RSOD remote sensing images, and VisDrone UAV images) have demonstrated the effectiveness of four improvements specifically for small-size object detection. Visualizations through Grad-CAM further demonstrate AeroDetectNet’s capacity to extract and focus on key object features. Upon individual testing across three open-source datasets, AeroDetectNet has successfully demonstrated its ability to identify objects in images with a smaller pixel area. Through experimental comparisons with other related studies, the AeroDetectNet achieved a competitive mAP while maintaining fewer model parameters, highlighting its highly accurate and lightweight properties.

The rapid development iteration of YOLO models has spurred extensive research into specialized adaptations tailored for remote sensing object detection (RSOD). Typically, these adaptations involve modifying specific YOLO versions to excel in dataset-specific benchmarks. Such efforts primarily focus on enhancing model performance on specific datasets, a trend that, while beneficial for achieving high leaderboard standings, frequently overlooks the critical aspects of generalizability, scalability, and transferability. Consequently, there is a significant gap in the research concerning the development of universally applicable modules that are easy to adapt across various baseline YOLO models—an essential factor for practical deployment and widespread adoption in real-world applications. This study addresses these shortcomings by introducing a novel feature fusion method, Context Enhancement (CE), Weight Adjustment-based Spatial Attention (WA), and Channel Shuffling (CS) — collectively termed CE-WA-CS, specifically designed for RSOD. This research rigorously tests the proposed feature fusion method across various YOLO models, substantiating its versatility and effectiveness in enhancing remote sensing object detection. Notably, in comparative performance evaluations involving several YOLO architectures, integrating our feature fusion method with YOLOv8 emerged as the most balanced in terms of accuracy, computational complexity, and inference speed. This optimal combination has been designated as YOLORemote. YOLORemote not only exemplifies a significant improvement in performance metrics but also demonstrates a practical balance that is crucial for real-time applications.

Small-object detection in remote sensing remains challenging due to low-resolution, weak cues, and complex backgrounds. Although semantic-context enhancement (SCE) methods leverage spatial priors effectively, their effectiveness is often limited by the stage, which contextual features are integrated. When introduced in the backbone, contextual information is subsequently degraded by the neck’s repeated resampling and fusion operations. Conversely, integrating context modeling mechanisms between the neck and the detection head is also suboptimal, as the features have already undergone multiscale mixing and spatial transformations, resulting in weakened semantic representation. To address this issue, we propose YOLO-feature supplement (FeatSup), a context-preserving detector built upon YOLOv11, which supplements the detection head with preserved semantic features via a dedicated bypass path. Specifically, a global−local spatial aggregation (GLSA) module is integrated in the backbone to capture high-fidelity semantic features before scale mixing, while the scale sequence (ScalSeq) fusion module performs effective multiscale feature fusion and delivers the enriched features directly to the small-object detection branch, thereby avoiding the degradation typically caused by repeated resampling and cross-scale operations in the feature aggregation process. Experiments on six public datasets demonstrate that YOLO-FeatSup achieves superior small-object detection accuracy with reduced computational cost, offering an efficient solution for remote sensing applications.

The objective of this study is to investigate the methods for soil liquefaction discrimination. Typically, predicting soil liquefaction potential involves conducting the standard penetration test (SPT), which requires field testing and can be time-consuming and labor-intensive. In contrast, the cone penetration test (CPT) provides a more convenient method and offers detailed and continuous information about soil layers. In this study, the feature matrix based on CPT data is proposed to predict the standard penetration test blow count N. The feature matrix comprises the CPT characteristic parameters at specific depths, such as tip resistance q_c, sleeve resistance f_s, and depth H. To fuse the features on the matrix, the convolutional neural network (CNN) is employed for feature extraction. Additionally, Genetic Algorithm (GA) is utilized to obtain the best combination of convolutional kernels and the number of neurons. The study evaluated the robustness of the proposed model using multiple engineering field data sets. Results demonstrated that the proposed model outperformed conventional methods in predicting N values for various soil categories, including sandy silt, silty sand, and clayey silt. Finally, the proposed model was employed for liquefaction discrimination. The liquefaction discrimination based on the predicted N values was compared with the measured N values, and the results showed that the discrimination results were in 75% agreement. The study has important practical application value for foundation liquefaction engineering. Also, the novel method adopted in this research provides new ideas and methods for research in related fields, which is of great academic significance.

This letter proposes AccuLiteFastNet, a novel object detection model tailored for aerial remote sensing scenarios constructed on the YOLOv8. AccuLiteFastNet is designed to enhance the precision in detecting small-size objects while maintaining a lightweight model framework and supporting fast inference speeds for deployment. Specifically, we use the normalized Gaussian Wasserstein distance (NGWD) as the loss metric to evaluate the similarity between the predicted and ground-truth object bounding boxes, ensuring uniform sensitivity for objects of different sizes. Moreover, we replace the model backbone’s last feature extraction module with the self-designed multiscale FasterNet (MSFN) module to reduce the model complexity while providing an expanded receptive field. Finally, we propose a novel module named context aggregation-C2f (ContA-C2f) specifically designed to evaluate the informativeness of each pixel in the image. Across three open-source remote sensing datasets, AccuLiteFastNet demonstrates a significant improvement over the original YOLOv8, achieving a 4.5% increase in mean average precision (mAP). In comparison to existing object detection models, AccuLiteFastNet strikes an optimal balance with high inference speed (58.14 fps) and good accuracy (87.5% mAP) at lower computational costs (8.3 GFLOPs).