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To overcome the challenges of obstacle avoidance for Unmanned Aerial Vehicles (UAVs) in autonomous flights, this paper proposes the Dual Experience Attention Convolution Soft Actor-Critic (DAC-SAC) algorithm. This algorithm integrates a dual experience buffer pool, a self-attention mechanism, and the Soft-Actor-Critic algorithm with a convolutional network. The dual experience buffer pools are used to solve the problem of ineffective UAV training due to the scarcity of successful training data. To overcome the drawbacks of the original Soft Actor-Critic (SAC) algorithm in handling image data, a Convolutional Neural Network (CNN) is applied to reconstruct the actor and critic network, allowing for better image feature extraction and classification. Furthermore, a self-attention mechanism is employed by adding a convolutional self-attention layer to the network. This modification enables dynamic adjustments for the attention weights based on varying input image features, effectively addressing focus-related challenges. Two simulation experiments are performed and the DAC-SAC algorithm achieves a 99.5% success rate in a known environment and an 84.8% success rate when dealing with an unknown environment. These results confirm that the proposed algorithm enables autonomous obstacle avoidance for UAVs even when considering depth images as input. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low recognition accuracy and large calculation amount of age estimation, this paper proposes a staged single aggregation backbone network VoVNetv4. In order to improve the feature extraction ability of the network, the Regional One-Shot Aggregation (ROSA) module is proposed in the network for regional encapsulation of the feature module. The classifier adopts the COnsistent RAnk Logits (CORAL) framework, which can solve the problem of inconsistency of classifiers in ordered regression CNNs for multiple binary classification tasks. This paper conducts experimental verification on the Asian Face Age Dataset (AFAD) and compares it with other research methods. The experimental results show that VoVNetv4 can effectively improve the feature extraction and detection ability of the network and improve the detection efficiency of age estimation. [ABSTRACT FROM AUTHOR]

In recent years, the Corona Virus Disease 2019 (Covid-19) epidemic has raged around the world, with more than 500 million people diagnosed. Relevant medical research and analysis results on Covid-19 indicate that wearing masks is an effective method to prevent and restrain virus transmission. Mask detection stations have been set up in hospitals, railway stations, schools, where there is large crowd flow, but results are not as good as expected. In order to ameliorate pandemic preventing and control measures, a mask wearing detection algorithm YOLOv3-M3 was designed and proposed in this paper. The algorithm can effectively detect people without mask, while consequently reminding them. Firstly, we substituted the feature extraction network of YOLOv3 with MobileNetv3, a lightweight convolutional neural network. Secondly, we utilized K-Means++ to substitute the original ground truth clustering algorithm to improve prediction precision. In addition, the bounding box regression loss function was revised as CIoU loss function. This loss function solves the issues of overlapping between the ground truth and the anchor box, which has increased the training speed. After experiments, the precision of YOLOv3 algorithm on mAP 0.5 and mAP 0.75 is 93.5% and 71.9%, respectively. Elevating 3.1% and 2.6%, respectively, higher than that of YOLOv3 algorithm, and it was superior to SSD, SSD Lite, YOLOv3-Tiny and other one-stage object detection algorithms. The detection speed can reach 13.6 frame/s, which has met the requirements of pandemic prevention and control in most places and can be deployed on terminal devices for object detection. [ABSTRACT FROM AUTHOR]

Road traffic monitoring is important for intelligent transportation, and researchers have begun to focus on the detection of traffic events using acoustic information. In this paper, we apply model fusion to traffic acoustic event classification. First, an improved, two-channel convolutional neural network (CNN) model is proposed as the weak classifier for constructing the fusion model. The mel-cepstral feature and its first-order and second-order difference are selected as the input features. Six different input features are constructed after signal preprocessing and segmentation. Second, after training six different CNN models, the voting method and support vector machine (SVM) stacking method are used to construct the final fusion model. Experimental results demonstrate that the detection rate of traffic acoustic events reaches 95.1%, which is higher than that of traditional traffic detection algorithms. [ABSTRACT FROM AUTHOR]