The proposed method is also evaluated on several benchmarks. Evaluation using our in-house dataset shows that the proposed framework achieves an F-score of 91.37 in the detection stage and the accuracy of human estimation error within 0.3 cm in the recognition stage is 95.37%, respectively.
In this work, an improved CTransformer is designed to retain sufficient global context information and extract more differentiated features for sequence recognition via multi-head self-attention.
Recently, the design of vision backbone using self-attention becomes an exciting topic. Then, asymmetric convolution Resnet-50 is used to extract multi-local information to effectively recognize inconsistent character sizes caused by different shooting angles of WLRs. First, a dual-attention mechanism to obtain the global information is introduced to better predict semantic segmentation feature maps and corner information. This paper proposes a novel dual-attention CornerNet for WLR image extraction and CTransformer for WLR sequence recognition. In addition, due to the influence of water surface reflection, it is not easy to extract the water level ruler (WLR) on the water surface accurately. However, water level recognition based on image processing faces illumination, shooting angle, and sediment contamination challenges. Image processing-based water level detectors have promising practical application value in intelligent agriculture and early water logging alerts. The proposed approach can sense waterlogging events at a national scale and provide an efficient and highly scalable alternative to conventional waterlogging sensing methods. These innovative reading rulers come in a variety of colours and are made from a combination of opaque and transparent plastic that both underlines text and. The system could sense approximately 2379 vision sources through an internet of video things framework and transmit the event-location information in 5 min. The use of a deep sensing system in the monsoon season in Taiwan was demonstrated, and waterlogging events were predicted on the island-wide scale. Therefore, in this study, a visual sensing approach driven by deep neural networks and information and communication technology was developed to provide an end-to-end mechanism to realize waterlogging sensing and event-location mapping. The use of an efficient large-scale waterlogging sensing and information system can provide valuable near real-time disaster information to facilitate disaster management and enhance awareness of the general public to alleviate losses during and after flood disasters. In the monsoon season, sudden flood events occur frequently in urban areas, which hamper the social and economic activities and may threaten the infrastructure and lives.