With the increase in wind power generation, wind turbine blades require regular inspections to ensure they continue to operate safely. You only look once (YOLO) is one of the most widely used object detection algorithms and is easy to deploy into drone devices. To enhance the real-time detection of small target defects in wind turbine blades, this paper proposes an improved attention and feature balanced YOLO (AFB-YOLO) algorithm based on YOLOv5s. Specifically, AFB-YOLO improves the feature pyramid network by using weighted feature fusion and cross-scale connections. The improved feature pyramid network solves the problem that most previous feature pyramid networks treat all input features equally, and obtains more feature information. Furthermore, the coordinate attention (CA) module is introduced into the network to augment the representations of the objects of interest. Finally, the paper redesigned the loss function through efficient intersection over union (EIoU) loss to make the model obtain a better localization effect. The experimental results on the imagery of wind turbine blade defects indicate that our method shows significant gains in performance. The mean average precision (mAP50) of AFB-YOLO is 83.7% and the detection accuracy is improved by 4.0% compared to the original YOLOv5s model. The experiments in this paper demonstrate that AFB-YOLO is more effective and robust than state-of-theart detectors.INDEX TERMS wind turbine blade, YOLOv5s, feature fusion, coordinate attention, EIoU loss.

Accurate target detection technology on ships can improve the comprehensive perception ability of weapon equipment. For SAR ship target detection in complex environments, false and missing alarms are serious. We design a new real-time ship target detection algorithm 3S-YOLO in SAR images. Firstly, reconstruct the network structure, adjust the relationship between receptive field and multiscale fusion, and realize the lightweight processing of feature extraction network and feature fusion network. Then, the network is pruned and compressed by the FPGM pruning algorithm to accelerate the reasoning speed. Finally, the Varifocal-EIoU loss function is designed to balance the positive and negative samples and overlapping losses and highlight the contribution of positive samples. To verify the effectiveness of the 3S-YOLO algorithm, verification is carried out in public datasets SSDD and HRSID. The results show that the accuracy of the model can be improved to 99.2% and 95.6%, respectively, after optimization. After pruning, the model volume decreased significantly and could be compressed to 190 KB. Model reasoning time can be reduced to less than 3 ms. Compared with the current mainstream algorithms, 3S-YOLO has achieved good results in all aspects to meet the real-time ship target detection in SAR images.

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Location Based Services (LBS) have become a popular technology to retrieve information about the surroundings of a mobile user which results in ubiquitous demand of spatial information service with diverse needs of different types of users. The aim of this paper is to reveal the potential of cloud-based spatial information service architecture that plays an integral role in LBS design and practice. This paper analyzes the characteristics of the spatial information of cloud services, such as data access transparency, spatial analysis parallelization, service capabilities flexible, information services standardization, service aggregation visualization, re-development flexible. This paper provides a possible solution to overcome the LBS service issues. In this paper, the short review of LBS and Cloud computing are given first and then the possibility of LBS design with cloud computing are analyzed. At last, cloud-based spatial information service architecture is proposed.