Whenever a forest fire occurs, it causes significant damage to the natural environment as well as to people's health and safety. The earlier the fire is detected, the more effective it is to control the fire and reduce the damage. To detect forest fires quickly and efficiently, this paper proposes an improved algorithm based on YOLOv7-tiny to fulfill accuracy and real-time requirements. SPD-Conv replaces the traditional stepwise convolution and pooling layer in YOLOv7-tiny to reduce computational complexity without sacrificing accuracy, and the accuracy has been slightly improved. GSConv is utilized to perform Slim-Neck operation on the feature tensor in the Neck part of the model, which minimizes the calculation cost and ensures computational accuracy at the same time. In addition, in order to exclude the influence of fog on the image in the real environment, we use a combination of dark-channel a priori algorithm and histogram equalization in the preprocessing stage to realize the image defogging operation. The experimental data show that the improved YOLOv7-tiny model achieves 3.5% improvement in correctness, 4.2% improvement in recall, and 6.7% improvement in FPS compared with the original YOLOv7-tiny model, which achieves a better balance between real-time and correctness than other models.
Image inpainting aims to recover the missing regions in an image and reconstruct a satisfactory restoration result with high quality. To solve the problem that the existing image inpainting methods do not deal with the information of missing and non-missing regions flexibly, and the global and local restoration semantics are inconsistent, we design a three-stage restoration model for the different semantic information required for restored regions at different scales, which utilizes different sizes of receptive fields to provide better image details at multiple scales, including global and local, and ensures semantic consistency of contextual information. Experimenting with our method on three popular publicly available image drawing datasets, the results show that this paper's method outperforms current restoration models.
The issue of inadequate precision in identifying airplanes in remote sensing images arises from fluctuations in lighting conditions. To tackle this problem, a refined approach for detecting targets in remote sensing using the YOLOv3 algorithm is proposed in this study. Primarily, a CBAM attention module (Convolutional block attention module) is introduced to emphasize crucial regions of the input object, thereby obtaining more significant information. Secondly, the Darknet53, acting as the backbone network in YOLOv3, is substituted with ResNet152 to enhance the network's capability in extracting features. Experimental results reveal that the algorithm achieves 92.75% mAP in the DOTA dataset, addressing the issue of insufficient detection accuracy caused by lighting variations in remote sensing images to a certain extent. This outcome demonstrates the feasibility and efficacy of the algorithm.
This paper proposes an improved GAN-based super-resolution (SR) network to address the issues of detail loss and low feature utilization in remote sensing image SR reconstruction. First, we propose a residual block (ERDB) containing multi-scale receptive fields (MRFs) to fully capture features at different scales, and use the attention module to dynamically adjust the features. In the discriminator network, we use the relative discriminator to compute the relative probability instead of the absolute probability and incorporate an attention module to help generate images containing more texture details. Experimental results demonstrate that our method has improved objective evaluation metrics on both UC Merced and NWPU-RESISC4 datasets and achieved good visual effects.
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