Braille character detection helps communication between normal and visually impaired people. The existing Braille detection methods are all aimed at scanning Braille document images while ignoring natural scene Braille images and CNN shining in the field of pattern recognition is rarely used for Braille detection. Firstly, a natural scene Braille image data set named NSBD was constructed. Then, an anchor-free Braille character detection based on the edge feature was proposed by analyzing that Braille characters in natural scene images that are relatively small in size, and a Braille character is composed of Braille dots that werelocated at the edge region of Braille character. Finally, the performance of the proposed method and other classic methods based on CNN was compared on NSBD. The experimental results show that the proposed method has good performance.

Underwater images suffer from color distortion, low contrast and blurring caused by the attenuation, refraction, and scattering of light. For many maritime operations, underwater image enhancement is essential. This paper proposes an underwater image enhancement method based on transformer, attention and multi-color-space inputs. First, transformer block is embedded into ResNet-50 as the backbone network. Combining this backbone network and multi-scale feature fusion forms the fundamental framework of our method. This framework contributes to mine the rich features of different scene underwater image and outputs a feature map of the same size as the input image. Then, multi-dimensional attention mechanism is added to the feature maps of different scales to mine key areas that require enhancement in underwater images and calculate the enhancement value of each pixel. Finally, multi-color-space inputs including RGB, HSV and LAB as the inputs for CNN to enhancement images from various aspects such as color deviation, brightness and saturation. The comparison of image quality evaluation metrics, visual enhancement performance evaluation and impact on the performance of underwater object detection with other underwater enhancement methods on datasets including SUIM-E, UIEB, EUVP, RUIE-RIQS, RUIE-UCCS and URPC-2018 prove the good performance of our underwater image enhancement method.

In agricultural planting, early plant classification is an indicator of crop health and growth. In order to accurately classify early plants, this paper proposes a classification method combining a dual attention mechanism and multi-scale module. Firstly, the ECA module (Efficient channel attention) is added to enhance the attention of the network to plants and suppress irrelevant background noise; secondly, the MSFN (Multi-scale Feedforward Network) module is embedded to improve the ability to capture complex data features. Finally, CA (Channel attention) is added to further emphasize the extracted features, thus enhancing the discrimination ability and improving the accuracy of the model. The experimental results show an accuracy of 93.20%, precision of 94.53%, recall of 93.27%, and an F1 score of 93.39%. This study can realize the classification of early plants, and effectively distinguish crops from weeds, which is helpful to identify and realize accurate weeding, thus promoting the intelligent and modern process of agricultural production.