The detection of traffic objects in aerial scenes holds significant application potential in both military and civilian sectors. However, current aerial traffic object detection techniques based on computer vision face challenges including limited awareness of object direction, a heavy computational burden on the feature extraction backbone network, and inadequate capacity to learn crucial semantic information. In this paper, our focus is on investigating the mechanisms for predicting the directional perception of traffic objects in aerial scenes, achieving backbone network lightness, and exploring methods for extracting key semantic information from objects. Firstly, to tackle the challenge of poor perception of traffic object direction and angle in aerial scenes, we utilize techniques like equivariant vector field convolution, multi-task anchor-free prediction, and adaptive loss to develop a precise mechanism for recognizing and predicting object directions. Secondly, given the presence of small-sized and numerous objects in aerial scenes, we propose the adoption of a lightweight backbone network employing channel stacking to decrease the model’s computational burden. Additionally, we establish a theoretical framework and methodology for optimizing and compressing this backbone network, aimed at enhancing feature extraction and propagation for aerial traffic objects. Furthermore, to address the issue of inadequate learning of key semantic information features, we incorporate saliency attention and multi-scale contextual information to capture the essential semantic characteristics of the objects. We also establish a method for extracting semantic features specifically for aerial traffic objects. The approach presented in this paper broadens the applicability of aerial object detection algorithms and offers novel methodologies and theoretical foundations for object detection in intricate scenarios.

Vehicle detection in aerial photography scenarios has a wide range of promising applications in the military and civilian fields. Recently, object detection algorithms based on depth models have shown superior performance in aerial vehicle detection tasks. However, these detection algorithms are often accompanied by a large amount of computation and resource consumption, which leads to the inability to perform real-time detection. In addition, the insufficient feature extraction capability of the vehicle and the complex background information also lead to low detection accuracy. In this letter, we propose a lightweight backbone network with a context information module and an attention mechanism module for vehicle detection in the aerial image, which enables the feature extraction network to increase the utilization of contextual information and salient regions. In addition, we use adaptive anchor-free in the detection model to predict the bounding box. The proposed detection algorithm achieves 89.7% and 94.1% mean average precision (mAP) on the German Aerospace Center (DLR)-3K dataset and the created dataset, and the detection time for each image is 1.66 and 0.049 s, respectively.

Vehicle object detection in aerial scenes has important applications in both military and civilian fields. Recently, deep learning has shown clear advantages in object detection, and the detection performance has been continuously improved. However, these deep object detection algorithms rely on anchor-based approaches accompanied by complex convolutional operations. In this paper, we establish a lightweight aerial vehicle object detection algorithm based on the method of anchor-free. The anchor-free based object detection method effectively gets rid of the limitation of detection model capability by the size of fixed anchor box, which reduces the set of parameters and provides a more flexible solution space. In addition, the proposed lightweight object feature extraction network effectively reduces the computational cost of the model, while improving the feature extraction capability of small objects. Besides, we use channel stacking to improve the object feature extraction capability of the lightweight network, and introduce the attention mechanism in the detection model to improve the efficiency of resource utilization. We evaluate the proposed detection algorithm on both the public aerial dataset and our collected aerial dataset, and the results show that our algorithm has significant advantages over other detection algorithms in detection accuracy and efficiency. The proposed detection algorithm achieves 89.1% and 92.6% mAP on the Munich dataset and the created dataset, and the detection time for each image is 1.21s and 0.036s, respectively.

Now, mobile applications grow at an exponential speed and their evolution activities are very active, while there is little research on the evolution of mobile apps. To have a better understanding of the evolution of mobile apps and find similarities or patterns in their evolution process, we conduct an empirical study on long spans in the lifetime of 8 typical open-source mobile apps, which covers 348 official releases. First, we try to verify whether Lehman's laws still apply to mobile apps or not, extract a variety of metrics of the apps, and use statistical hypothesis testing to validate these laws. We find enough data that support a subset of Lehman's laws, while the rest do not. Second, we make some novel observations, eg, the growth of mobile apps is nonsmooth, and some versions of the apps have a great growth in their evolution. Enough data confirming that software instability increases great with the addition of third-party method invocations, and automatic build and manage tool based on contract is introduced into project as apps continue evolving.