Remote sensing image change detection (RSCD) aims to identify differences between remote sensing images of the same location at different times. However, due to the significant variations in the size and appearance of objects in real-world scenes, existing RSCD algorithms often lack strong capabilities in extracting multiscale features, thereby failing to fully capture the characteristics of changes. To address this issue, a multiscale remote sensing change detection network (MSNet) and a multiscale RSCD dataset (MSRS-CD) are proposed. A multiscale convolution module (MSCM) is investigated, and combined with MSCM, an encoder capable of capturing features of different sizes is designed to efficiently extract multiscale semantic change features. A global multiscale feature fusion module is designed to achieve global multiscale feature fusion and obtain multiscale high-level semantic change features. As existing RSCD datasets lack rich scale information and often focus on change targets of specific sizes, a new dataset, MSRS-CD, is constructed. This dataset consists of 842 pairs of images with a resolution of 1024×1024 pixels, featuring uniformly distributed change detection target sizes. Comparative experiments are conducted with ten other state-of-the-art algorithms on the MSRS-CD dataset and another public dataset, LEVIR-CD. Experimental results demonstrate that MSNet achieves the best performance on both datasets, with an F1 score of 75.74% on the MSRS-CD dataset and 91.41% on the LEVIR-CD dataset.

Smoking behavior in public places and fire bans seriously threatens the safety of people's lives and property. In order to ensure public safety, this paper proposes a smoking behavior detection method based on YOLOv5 algorithm and image processing. Aiming at the small target of cigarettes, this paper uses the K-means algorithm and the method of adding a small target detection layer to improve the YOLOv5 algorithm, and realizes the improvement of the detection accuracy. On the self-made data set, the false detection rate is 0%, and the AP is 92.3%, which is 6.7% higher than that of the YOLOv5s algorithm.

In the processing of high-resolution remote sensing images, multi-scale feature fusion techniques are commonly employed to construct change detection models, aiming to capture the details and characteristics of target objects at different scales. However, current change detection methods often neglect the subtle features of small-scale targets and edge information during feature fusion. To address this issue, this paper proposes a Deep Feature Fusion Network (DFFNet) for full-scale change detection in remote sensing images. DFFNet enhances the accuracy of boundary information in change regions and enables the extraction of full-scale change features. By integrating phased difference extraction techniques with an enhanced attention mechanism, a Dual Temporal Difference Enhancement Module (DT-DEM) is designed to comprehensively extract change detection information across scales. Additionally, combining an inverted pyramid technique with an attention mechanism, a Screening Function Inverted Pyramid Network (S-FIPN) is proposed, which efficiently extracts and fuses full-scale features while significantly improving the extraction of subtle changes and weak edge features.Comparison experiments with ten state-of-the-art algorithms on four publicly available datasets, namely, LEVIR-CD, WHU-CD, NJDS and MSRS-CD, show that DFFNet achieves the best F1 values on all datasets, which are 92.50%, 90.55%, 81.04% and 77.44%, respectively.These results underscore the superiority and effectiveness of DFFNet in full-scale feature extraction and edge information retrieval.

As a key technology in the intelligent interpretation of remote sensing, remote sensing image change detection aims to automatically identify surface changes from images of the same area acquired at different times. Although vision foundation models have demonstrated outstanding capabilities in image feature representation, their inherent patch-based processing and global attention mechanisms limit their effectiveness in perceiving multi-scale targets. To address this, we propose a multi-scale remote sensing image change detection network based on a vision foundation model, termed SAM-MSCD. This network integrates an efficient parameter fine-tuning strategy with a cross-temporal multi-scale feature fusion mechanism, significantly improving change perception accuracy in complex scenarios. Specifically, the Low-Rank Adaptation mechanism is adopted for parameter-efficient fine-tuning of the Segment Anything Model (SAM) image encoder, adapting it for the remote sensing change detection task. A bi-temporal feature interaction module(BIM) is designed to enhance the semantic alignment and the modeling of change relationships between feature maps from different time phases. Furthermore, a change feature enhancement module (CFEM) is proposed to fuse and highlight differential information from different levels, achieving precise capture of multi-scale changes. Comprehensive experimental results on four public remote sensing change detection datasets, namely LEVIR-CD, WHU-CD, NJDS, and MSRS-CD, demonstrate that SAM-MSCD surpasses current state-of-the-art (SOTA) methods on several key evaluation metrics, including the F1-score and Intersection over Union(IoU), indicating its broad prospects for practical application.