In this work, we propose a novel one-stage and keypoints-based framework for monocular 3D objects detection using only RGB images, called KM3D-Net. 2D detection only requires a deep neural network to predict 2D properties of objects, as it is a semanticity-aware task. For image-based 3D detection, we argue that the combination of the deep neural network and geometric constraints are needed to estimate appearance-related and spatial-related information synergistically. Here, we design a fully convolutional model to predict object keypoints, dimension, and orientation and then combine these estimations with perspective geometry constraints to compute position attributes. Further, we reformulate the geometric constraints as a differentiable version and embed it into the network to reduce running time while maintaining the consistency of model outputs in an end-to-end fashion. Benefiting from this simple structure, we then propose an effective semi-supervised training strategy for the setting where labeled training data is scarce. In this strategy, we enforce a consensus prediction of two shared-weights KM3D-Net for the same unlabeled image under different input augmentation conditions and network regularization. In particular, we unify the coordinate-dependent augmentations as the affine transformation for the differential recovering position of objects and propose a keypoints-dropout module for the network regularization. Our model only requires RGB images without synthetic data, instance segmentation, CAD model, or depth generator. Nevertheless, extensive experiments on the popular KITTI 3D detection dataset indicate that the KM3D-Net surpasses all previous state-of-the-art methods in both efficiency and accuracy by a large margin. And also, to

To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach.

Automatic segmentation of the lungs in Chest X-ray images (CXRs) is a key step in the screening and diagnosis of related diseases. There are many opacities in the lungs in the CXRs of patients, which makes the lungs difficult to segment. In order to solve this problem, this paper proposes a segmentation algorithm based on U-Net. This article introduces variational auto-encoder (VAE) in each layer of the decoder-encoder. VAE can extract high-level semantic information, such as the symmetrical relationship between the left and right thoraxes in most cases. The fusion of the features of VAE and the features of convolution can improve the ability of the network to extract features. This paper proposes a three-terminal attention mechanism. The attention mechanism uses the channel and spatial attention module to automatically highlight the target area and improve the performance of lung segmentation. At the same time, the three-terminal attention mechanism uses the advanced semantics of high-scale features to improve the positioning and recognition capabilities of the attention mechanism, suppress background noise, and highlight target features. Experimental results on two different datasets show that the accuracy (ACC), recall (R), F1-Score and Jaccard values of the algorithm proposed in this paper are the highest on the two datasets, indicating that the algorithm in this paper is better than other state-of-the-art algorithms.