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Abstract:Aiming at the problem of low fall detection accuracy and poor real-time performance in indoor scenes due to the effects of light change, occlusion of the human body form, and changes in the human body posture under special viewpoint, a lightweight improved fall detection algorithm based on YOLOv8, named FDW-YOLO, is proposed. The C2f module in the backbone network is replaced by the FasterNext module, which reduces the computational complexity while retaining the excellent feature extraction capability. According to the characteristics of human falls with large changes in posture, three network structures with dynamically deformable convolutional modules added at different positions in the neck layer are designed, experiments are conducted on a self-made fall dataset for comparison, and ultimately, the YOLOv8-C scheme is selected based on network performance. A bounding box regression loss function WIoU is introduced into the improved network to replace the original CIoU. The experimental results show that compared with YOLOv8n, the FDW-YOLO fall detection algorithm increases mAP@0.5 from 96.5% to 97.9% and mAP@0.5:0.95 from 72.5% to 74.3%, while the number of parameters and computation is only 4.1x106 and 7.3x109, which is in line with the requirements for deployment in low-computing power industrial scenarios. [ABSTRACT FROM AUTHOR]

The single-stage object detection technology represented by YOLOv8 has significant optimizations in the backbone network, but fails to efficiently integrate contextual information in the neck network, leading to missed and false detections in small object detection. Additionally, the large number of algorithm parameters and high computational complexity make it unsuitable for end-to-end industrial deployment. To address these issues, this paper introduce the BiFormer attention mechanism based on the Transformer structure to enhance the detection performance for small objects and improve the algorithm's accuracy. At the same time introduce the GSConv module to reduce the algorithm size while ensuring no adverse impact on its performance, balancing the increase in computational and parametric costs brought by BiFormer. An object detection algorithm named Bi-YOLO is designed to achieve a balance between lightweight and algorithm performance. Experimental results show that compared to YOLOv8n, the Bi-YOLO object detection algorithm improves algorithm accuracy by 4.6%, increases the small object detection accuracy on the DOTA dataset by 2.3%, and reduces the number of parameters by 12.5%. Bi-YOLO effectively achieves a balance between algorithm lightweight and performance, providing a new approach for end-to-end industrial deployment. [ABSTRACT FROM AUTHOR]

Cloud computing can play an important role in software testing technology because of its high reliability and the benefits from on-demand access service pattern. It can also provide a new solution for hard to deploy problems in software testing with high efficiency and low cost. A novel idea called "testing as service" is proposed, which adopts the idea of "software as service" and extend its advantage into testing. The testing of service is a layered software testing architecture based on the Hadoop cloud computing platform. A competitive experiment is conducted to evaluate the load efficiency. The results demonstrate its fast response time and good performance in distributing the workloads when receiving requests from multiple users. [ABSTRACT FROM AUTHOR]