With the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.

Target detection plays a crucial role in the intelligent interpretation of remote sensing images and has a wide range of potential applications. However, in the presence of targets with high aspect ratios and significant scale variations in remotely sensed images, existing methods prefer CNN or transformer architectures but suffer from the limitations of overly fixed receptive fields or excessive computational complexity. Recently, Mamba-based methods have become hot in the field of target detection and show significant potential in capturing remote dependencies with linear complexity but lack in-depth customization for remote sensing targets. To address the above challenges, we propose a new target detection framework for complex remote sensing images, LS-MambaNet. Specifically, firstly, a group fusion strategy is combined with the introduction of large-band convolution to adaptively adjust the receptive domains of the features, which enhances the spatial context information extraction for objects with high aspect ratios. In addition, a Multi-Granularity Spatial Mamba Block is proposed, and this employs a multi-granularity scanning strategy to reduce the computational cost and feature redundancy on different scanning paths and is able to efficiently model the global contextual information of the target. Experimental results show that LS-MambaNet outperforms baselines on DOTA1.0 and HRSC2016 datasets. In particular, LS-MambaNet significantly improves the inference speed and achieves a higher FPS while maintaining state-of-the-art detection accuracy.

Object detection is a fundamental task in computer vision. To improve the detection accuracy of a detection model without increasing the model weights, this paper modifies the YOLOX model by first replacing some of the traditional convolution operations in the backbone network to reduce the parameter cost of generating feature maps. We design a local feature extraction module to chunk the feature maps to obtain local image features and a global feature extraction module to calculate the correlation between feature points to enrich the feature, and add learnable weights to the feature layers involved in the final prediction to assist the model in detection. Moreover, the idea of feature map reuse is proposed to retain more information from the high-dimensional feature maps. In comparison experiments on the dataset PASCAL VOC 2007 + 2012, the accuracy of the improved algorithm increased by 1.2% over the original algorithm and 2.2% over the popular YOLOv5.

In recent years, traffic forecasting has gradually become a core component of smart cities. Due to the complex spatial-temporal correlation of traffic data, traffic flow prediction is highly challenging. Existing studies are mainly focused on graphical modeling of fixed road structures. However, this fixed graphical structure cannot accurately capture the relationship between different roads, affecting the accuracy of long-term traffic flow prediction. In order to address this problem, this paper proposes a modeling framework STN-GCN for spatial-temporal normalized graphical convolutional neural networks. In terms of temporal dependence, spatial-temporal normalization was used to divide the data into high-frequency and low-frequency parts, allowing the model to extract more distinct features. In addition, fine data input to the temporal convolutional network (TCN) was used in this module to conduct more detailed temporal feature extraction so as to ensure the accuracy of long-term sequence extraction. In addition, the transformer module was added to the model, which captured the real-time state of traffic flow by extracting spatial dependencies and dynamically establishing spatial correlations through a self-attention mechanism. During the training process, a curriculum learning (CL) method was adopted, which provided optimized target sequences. Learning from easier targets can help avoid getting trapped in local minima and yields better generalization performance to more accurately approximate global minima. As shown by experimental results the model performed well on two real-world public transportation datasets, METR-LA and PEMS-BAY.