The importance of panoramic traffic perception tasks in autonomous driving is increasing, so shared networks with high accuracy are becoming increasingly important. In this paper, we propose a multi-task shared sensing network, called CenterPNets, that can perform the three major detection tasks of target detection, driving area segmentation, and lane detection in traffic sensing in one go and propose several key optimizations to improve the overall detection performance. First, this paper proposes an efficient detection head and segmentation head based on a shared path aggregation network to improve the overall reuse rate of CenterPNets and an efficient multi-task joint training loss function to optimize the model. Secondly, the detection head branch uses an anchor-free frame mechanism to automatically regress target location information to improve the inference speed of the model. Finally, the split-head branch fuses deep multi-scale features with shallow fine-grained features, ensuring that the extracted features are rich in detail. CenterPNets achieves an average detection accuracy of 75.8% on the publicly available large-scale Berkeley DeepDrive dataset, with an intersection ratio of 92.8% and 32.1% for driveableareas and lane areas, respectively. Therefore, CenterPNets is a precise and effective solution to the multi-tasking detection issue.

Aiming at the problems of the difficult extraction of small target feature information, complex background, and variable target scale in unmanned aerial vehicle (UAV) aerial photography images. In this paper, an anchor-free target detection algorithm based on fully convolutional one-stage object detection (FCOS) for UAV aerial photography images is proposed. For the problem of complex backgrounds, the global context module is introduced in the ResNet50 network, which is combined with feature pyramid networks (FPN) as the backbone feature extraction network to enhance the feature representation of targets in complex backgrounds. To address the problem of the difficult detection of small targets, an adaptive feature balancing sub-network is designed to filter the invalid information generated at all levels of feature fusion, strengthen multi-layer features, and improve the recognition capability of the model for small targets. To address the problem of variable target scales, complete intersection over union (CIOU) Loss is used to optimize the regression loss and strengthen the model’s ability to locate multi-scale targets. The algorithm of this paper is compared quantitatively and qualitatively on the VisDrone dataset. The experiments show that the proposed algorithm improves 4.96% on average precision (AP) compared with the baseline algorithm FCOS, and the detection speed is 35 frames per second (FPS), confirming that the algorithm has satisfactory detection performance, real-time inference speed, and has effectively improved the problem of missed detection and false detection of targets in UAV aerial images.

Human chorionic gonadotropin (HCG) is an essential biomarker for the evaluation and diagnosis of early pregnancy, multiple pregnancies, and ectopic pregnancies. However, the accuracy of test strip interpretation is often compromised by inconvenient and uncomfortable professional testing, the black-box nature of AI-based detection methods, and variations in image quality caused by mobile photography and lighting conditions. To address these challenges, we propose 2SLOD-HCG, a novel network for test strip concentration detection. Our approach introduces an enhanced spatial pyramid pooling (SPP) module to better integrate multi-scale receptive field information and incorporates an elastic variational cross-FPN structure augmented with lightweight transformer blocks to strengthen global feature perception. Furthermore, a SimAM attention mechanism is applied to highlight critical local features. These improvements collectively enhance the network’s ability to capture both fine-grained and global contextual information. We constructed a dataset of 50,000 augmented test strip images collected under three lighting conditions and four mobile photography scenarios. The results demonstrate that 2SLOD-HCG achieves superior accuracy and robustness compared to existing YOLO-based baselines, particularly in detecting the small color-developing regions of test strips.

With the rapid development of robotics technology, robotic arm grasping has gained significant attention in the fields of automation and artificial intelligence. In this study, we propose a fractional-order deep deterministic policy gradient (DDPG) algorithm for optimizing robotic arm grasping tasks. Traditional machine learning algorithms face challenges in handling continuous action spaces, while the DDPG algorithm effectively addresses this issue. In this research, we first review the background and challenges of robotic arm grasping and provide an overview of the application of traditional reinforcement learning algorithms in grasping tasks. Subsequently, we introduce the principles and fundamental ideas of the DDPG algorithm in detail, discussing its potential for optimizing robotic arm grasping. To further enhance the performance of robotic arm grasping, we propose an improved approach based on fractional-order control. Fractional-order control exhibits unique advantages in environmental dynamics modeling and grasp posture optimization, enhancing the robustness and adaptability of robotic arm grasping. Through a series of experiments, we validate the effectiveness and superiority of the fractional-order DDPG algorithm in robotic arm grasping tasks. Our algorithm achieves significant improvements in grasping success rate and stability compared to traditional methods. The experimental results demonstrate that the fractional-order DDPG algorithm is better equipped to handle control challenges in continuous action spaces and optimize the performance of robotic arm grasping tasks.