Conference Agenda

In the application of target detection for marine oil spills, deep learning methods are gradually replacing traditional remote sensing image recognition approaches. While complex models designed for higher accuracy may compromise recognition speed, they often fail to meet the rapid response requirements of terminal device applications (Chai et al, 2025). Therefore, developing a lightweight detection model that balances high accuracy and real-time performance is crucial for enhancing marine oil spill emergency response capabilities (Liang et al, 2024). Based on the yolov8m model, this study introduces GSConv (Li et al, 2024) lightweight convolution and CCFM (Guo et al, 2025) cross-scale feature fusion module, which significantly improves the adaptability of multi-scale target detection and recognition accuracy in complex backgrounds while maintaining model lightweightness, thereby offering a novel and effective solution for marine oil spill target detection.

During the commissioning phase of ESA's Sentinel-2C, tandem images with Sentinel-2A were acquired with a delay of 30 seconds. We present a novel, automated method for labeling moving vehicles in Sentinel-2 images, leveraging the temporal offset between these tandem acquisitions. We propose a filtering process that isolates pixels corresponding to vehicles that moved between the two acquisitions. We generate a training dataset based on this process, removing the need for a large manual labeling phase. The dataset is used to train a standard deep-learning-based vehicle detection model. Experimental results, as well as a validation study using ground-truth data from California, highlight the quality of the proposed labeling method, and show that a vehicle detection model can be successfully trained from quasi-simultaneous acquisitions.

With the rapid development of aerial imaging technology, aerial target detection has become a research hotspot with broad applications in intelligent transportation, agricultural monitoring, and military surveillance. However, the performance of aerial detection models is often degraded under adverse weather conditions such as fog, sandstorms, and low illumination. In such environments, aerial images typically suffer from reduced contrast and color distortion, which significantly affects the model’s ability to accurately identify targets. To this end, a Lightweight All-in-One Aerial Detection Enhancer Under Adverse Weather (LAD-Enhancer) has been proposed. The designed enhancer processes and restores degraded aerial images, thereby enhancing the detection model’s ability to perceive potential targets. Unlike conventional image restoration models, LAD-Enhancer integrates detection labels as additional supervision during training to ensure that enhancement is detection-oriented rather than purely visual. Furthermore, a three-stage training strategy and a Mixture of Experts (MoE) framework are employed to adaptively classify and process images captured under different degradation conditions. Experimental results demonstrate that, with an increase of fewer than 3K parameters, the proposed LAD-Enhancer significantly improves detection performance under adverse weather conditions while maintaining almost unchanged performance on clear-weather images.

Currently, the state-of-the-art methods for detecting small unmanned aerial vehicles (UAVs) continue to struggle in complex urban settings due to several persistent challenges, namely, frequent target occlusion, high similarity in thermal radiation signatures between UAVs and their surroundings, and the inherently low visual saliency of small UAV targets, all of which contribute to degraded detection performance. To tackle these issues, this paper introduces a novel multi-modal feature fusion collaborative detection (MFFCD) framework grounded in learnable spatial mapping. The architecture consists of three key components: firstly, a multi-branch parallel feature extraction module (MBPFE) that simultaneously processes infrared, visible, and radar range-azimuth images, complemented by a feature fusion module (FFM) designed to enhance both intra-modal and inter-modal feature interactions; then, an adaptive spatially-aware dynamic detection head module (DDH) that dynamically recalibrates feature weights to strengthen target representation and boost detection accuracy; and a feature collaborative enhancement module (FCE) that employs a learnable affine transformation to align and fuse multi-modal features, thereby producing more robust and reliable detection outcomes. Extensive experiments show that the proposed MFFCD framework substantially outperforms existing methods under challenging urban conditions, achieving a 56.89% gain in Mean Average Precision (mAP) for small UAV detection.

Infrared and visible remote sensing image fusion is crucial for improving scene perception in complex environments, but existing autoencoder-based methods suffer from insufficient information interaction between modalities, inadequate deep feature fusion, and ineffective loss functions in extreme scenarios. To address these issues, this study proposes a Differential Feature Enhancement and Cross-modal Fusion (DFECF) method. The DFECF adopts an end-to-end architecture consisting of dual-stream encoders, cross-modal fusion modules, Transformer global perception modules, and decoders. Specifically, the Differential Enhancement (DE) module extracts differential information between infrared and visible features, combined with spatial and channel attention to enhance feature representation. The cross-modal fusion module adaptively integrates deep features based on channel attention, adjusting feature weights according to scene characteristics. The Transformer module supplements the global receptive field to capture long-range feature dependencies, and a joint loss function is designed to optimize fusion performance. Experimental results on public datasets show that the proposed method outperforms existing state-of-the-art methods in both subjective visual effects and objective evaluation metrics, especially in extreme environments such as strong light and thick smoke. It effectively improves the integrity of scene perception and provides high-quality data support for practical applications such as forest fire prevention, mining area monitoring, and autonomous driving.