Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at \href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.Comment: 32 pages, 18 figure

Adolescent, but Not Adult, Binge Ethanol Exposure Leads to Persistent Global Reductions of Choline Acetyltransferase Expressing Neurons in Brain

During the adolescent transition from childhood to adulthood, notable maturational changes occur in brain neurotransmitter systems. The cholinergic system is composed of several distinct nuclei that exert neuromodulatory control over cognition, arousal, and reward. Binge drinking and alcohol abuse are common during this stage, which might alter the developmental trajectory of this system leading to long-term changes in adult neurobiology. In Experiment 1, adolescent intermittent ethanol (AIE; 5.0 g/kg, i.g., 2-day on/2-day off from postnatal day [P] 25 to P55) treatment led to persistent, global reductions of choline acetyltransferase (ChAT) expression. Administration of the Toll-like receptor 4 agonist lipopolysaccharide to young adult rats (P70) produced a reduction in ChAT+ IR that mimicked AIE. To determine if the binge ethanol-induced ChAT decline was unique to the adolescent, Experiment 2 examined ChAT+ IR in the basal forebrain following adolescent (P28-P48) and adult (P70-P90) binge ethanol exposure. Twenty-five days later, ChAT expression was reduced in adolescent, but not adult, binge ethanol-exposed animals. In Experiment 3, expression of ChAT and vesicular acetylcholine transporter expression was found to be significantly reduced in the alcoholic basal forebrain relative to moderate drinking controls. Together, these data suggest that adolescent binge ethanol decreases adult ChAT expression, possibly through neuroimmune mechanisms, which might impact adult cognition, arousal, or reward sensitivity

Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer

With the rapid development of cloud manufacturing, industrial production with edge computing as the core architecture has been greatly developed. However, edge devices often suffer from abnormalities and failures in industrial production. Therefore, detecting these abnormal situations timely and accurately is crucial for cloud manufacturing. As such, a straightforward solution is that the edge device uploads the data to the cloud for anomaly detection. However, Industry 4.0 puts forward higher requirements for data privacy and security so that it is unrealistic to upload data from edge devices directly to the cloud. Considering the above-mentioned severe challenges, this paper customizes a weakly-supervised edge computing anomaly detection framework, i.e., Federated Learning-based Transformer framework (\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud manufacturing. Specifically, we introduce federated learning (FL) framework that allows edge devices to train an anomaly detection model in collaboration with the cloud without compromising privacy. To boost the privacy performance of the framework, we add differential privacy noise to the uploaded features. To further improve the ability of edge devices to extract abnormal features, we use the Transformer to extract the feature representation of abnormal data. In this context, we design a novel collaborative learning protocol to promote efficient collaboration between FL and Transformer. Furthermore, extensive case studies on four benchmark data sets verify the effectiveness of the proposed framework. To the best of our knowledge, this is the first time integrating FL and Transformer to deal with anomaly detection problems in cloud manufacturing