To enhance aerial image detection in complex environments characterized by multiple small targets and mutual occlusion, we propose an aerial target detection algorithm based on an improved version of YOLOv5 in this paper. Firstly, we employ an improved Mosaic algorithm to address redundant boundaries arising from varying image scales and to augment the training sample size, thereby enhancing detection accuracy. Secondly, we integrate the constructed hybrid attention module into the backbone network to enhance the model’s capability in extracting pertinent feature information. Subsequently, we incorporate feature fusion layer 7 and P2 fusion into the neck network, leading to a notable enhancement in the model’s capability to detect small targets. Finally, we replace the original PAN + FPN network structure with the optimized BiFPN (Bidirectional Feature Pyramid Network) to enable the model to preserve deeper semantic information, thereby enhancing detection capabilities for dense objects. Experimental results indicate a substantial improvement in both the detection accuracy and speed of the enhanced algorithm compared to its original version. It is noteworthy that the enhanced algorithm exhibits a markedly improved detection performance for aerial images, particularly under real-time conditions.

Point cloud semantic segmentation is of utmost importance in practical applications. However, most existing methods have evolved to be incredibly intricate, leading to a rise in complexity that has made them increasingly impractical for real-world utilization. The escalating complexity of these methods has resulted in a deterioration in their efficiency and ease of implementation, making them less suitable for use in time-sensitive and resource-constrained environments. Towards this issue, we propose an efficient and lightweight segmentation method, able to achieve a remarkable performance in terms of both segmentation accuracy, training speed, and space consumption. Specifically, we first propose to adopt random sampling to replace the original one to obtain more efficiency. Moreover, a lightweight decoding module and an improved bilateral enhancement (BAE) module are developed to further improve the performance. The proposed method achieved a 73.6% and 60.7% mIoU on the S3DIS and Semantickitti datasets, respectively. In the future, the random sampling and the proposed BAE module can be adopted in a more concise and lightweight network to achieve faster and more-accurate point cloud segmentation.

Automatic defect segmentation of atmospheric plasma sprayed (APS) thermal barrier coatings (TBCs) from scanning electron microscopy (SEM) images is essential for quantitative microstructural assessment; however, existing instance segmentation models often exhibit insufficient multi-scale representation and excessive computational cost. To address these limitations, we propose an improved YOLO11-seg–based framework that jointly optimizes accuracy and efficiency through two architectural enhancements: a Boundary-aware Recalibrated Bidirectional Pyramid Network (BRB-FPN) for strengthened cross-scale feature fusion and precise contour localization, and an Efficient Shared-Convolution Segmentation Head (ESCH) for reducing parameters and FLOPs while accelerating inference. Without introducing additional training strategies, the proposed method achieves consistent performance gains on a self-constructed APS–TBC SEM defect dataset, improving Mask mAP@50 by 6.9%, Mask mAP@50:95 by 4.4%, and recall by 5.5% over the baseline, while simultaneously lowering computational complexity and increasing FPS, and outperforming several mainstream segmentation approaches. These results demonstrate that the proposed framework provides an accurate and computationally efficient solution for industrial microscopic defect inspection.