By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.
Dimethylsulfoxonium propionate (DMSOP) is a recently identified and abundant marine organosulfur compound with purported roles in oxidative stress protection, global carbon and sulfur cycling1. Diverse algae and bacteria synthesise DMSOP from dimethylsulfoniopropionate (DMSP), which potentially limits the production of climate-active gases e.g., dimethylsulfide (DMS) generated from microbial DMSP cleavage1. Here, DMSOP was found at mM levels in saltmarsh sediment, >10-fold higher than DMSP, and orders of magnitude higher than DMSOP levels previously reported in seawater1. Moreover, we showed bacteria could utilise DMSOP as an osmoprotectant. Some bacteria also cleave DMSOP liberating dimethyl sulfoxide (DMSO), an ubiquitous marine metabolite and acrylate1, but the enzymes responsible and their environmental importance were unknown. Here, we elucidated the DMSOP cleavage mechanism/s in diverse heterotrophic bacteria, e.g., SAR11 clade and Roseobacters, and fungi and phototrophic algae, e.g., Emiliania huxleyi, not previously known to have this activity. All these diverse organisms utilised their DMSP lyase ‘Ddd’ or ‘Alma1’ enzymes, that span five protein families, to cleave DMSOP with similar specific activities to DMSP. Bacteria with DMSP lyases that used DMSP as a carbon source likewise used DMSOP. Furthermore, ddd gene transcription in these bacteria was induced by DMSOP, including dddK in SAR11 strain HTCC1062. We determined the structure of DddK bound to DMSOP and concluded that the catalytic mechanisms of DMSOP cleavage by Ddd enzymes were like those for DMSP. Given the predicted teragram DMSOP production budget1, its newly found abundance in marine sediments, and the abundance of microbial DMSP lyase genes and transcripts in marine environments, DMSOP cleavage is likely a globally significant process influencing global carbon and sulfur fluxes and marine ecological interactions.
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