Object detection in aerial images has always been a challenging task due to the generally small size of the objects. Most current detectors prioritize the development of new detection frameworks, often overlooking research on fundamental components such as feature pyramid networks (FPNs). In this article, we introduce the cross-layer feature pyramid transformer (CFPT), a novel upsampler-free FPN designed specifically for small object detection in aerial images. CFPT incorporates two meticulously designed attention blocks with linear computational complexity: cross-layer channelwise attention (CCA) and cross-layer spatialwise attention (CSA). CCA achieves cross-layer interaction by dividing channelwise token groups to perceive cross-layer global information along the spatial dimension, while CSA enables cross-layer interaction by dividing spatialwise token groups to perceive cross-layer global information along the channel dimension. By integrating these modules, CFPT enables efficient cross-layer interaction in a single step, thereby avoiding the semantic gap and information loss associated with elementwise summation and layer-by-layer transmission. In addition, CFPT incorporates global contextual information, which improves detection performance for small objects. To further enhance location awareness during cross-layer interaction, we propose the cross-layer consistent relative positional encoding (CCPE) based on interlayer mutual receptive fields. We evaluate the effectiveness of CFPT on three challenging object detection datasets in aerial images: VisDrone2019-DET, TinyPerson, and xView. Extensive experiments demonstrate that CFPT outperforms state-of-the-art FPNs while incurring lower computational costs. The code is available at https://github.com/duzw9311/CFPT

The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

A decentralized adaptive synchronization problem for a simple yet nontrivial discrete-time stochastic model of network dynamics is investigated, which also illustrates a general framework for a class of adaptive control problems for complex systems with uncertainties. To describe synchronization phenomena in noisy environments, several new definitions of synchronization for stochastic systems are given and applied in our model. In the framework proposed, we prove that in four different cases on local goals, including “deterministic tracking,” “center-oriented tracking,” “loose tracking,” and “tight tracking,” under mild conditions on noise sequence and communication limits, the agents in the considered model can achieve global synchronization in sense of mean by using local estimators and controllers based on a least-squares (LS) algorithm. These results show that agents in a complex system disturbed by noise with communication limits can autonomously achieve the global goal of synchronization by using local LS-based adaptive controllers while they are pursuing for their local goals.