As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes and small, densely packed targets, there are many challenges in detecting remote sensing objects. Those challenges lead to insufficient accuracy in remote sensing object detection. Consequently, developing a new model is essential to enhance the identification capabilities for objects in remote sensing imagery. To solve these constraints, we have designed the OD-YOLO approach that uses multi-scale feature fusion to improve the performance of the YOLOv8n model in small target detection. Firstly, traditional convolutions have poor recognition capabilities for certain geometric shapes. Therefore, in this paper, we introduce the Detection Refinement Module (DRmodule) into the backbone architecture. This module utilizes Deformable Convolutional Networks and the Hybrid Attention Transformer to strengthen the model’s capability for feature extraction from geometric shapes and blurred objects effectively. Meanwhile, based on the Feature Pyramid Network of YOLO, at the head of the model framework, this paper enhances the detection capability by introducing a Dynamic Head to strengthen the fusion of different scales features in the feature pyramid. Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. Experiments on the VisDrone dataset show that OD-YOLO surpasses the compared models by at least 5.2% in mAP50 and 4.4% in mAP75, and experiments on the Foggy Cityscapes dataset demonstrated that OD-YOLO improved mAP by 6.5%, demonstrating outstanding results in tasks related to remote sensing images and adverse weather object detection. This work not only advances the research in remote sensing image analysis, but also provides effective technical support for the practical deployment of future remote sensing applications.

Video anomaly detection (VAD) faces significant challenges in multimodal semantic alignment and long-term temporal modeling within open surveillance scenarios. Existing methods are often plagued by modality discrepancies and fragmented temporal reasoning. To address these issues, we introduce MT-CMVAD, a hierarchically structured Transformer architecture that makes two key technical contributions: (1) A Context-Aware Dynamic Fusion Module that leverages cross-modal attention with learnable gating coefficients to effectively bridge the gap between RGB and optical flow modalities through adaptive feature recalibration, significantly enhancing fusion performance; (2) A Multi-Scale Spatiotemporal Transformer that establishes global-temporal dependencies via dilated attention mechanisms while preserving local spatial semantics through pyramidal feature aggregation. To address the sparse anomaly supervision dilemma, we propose a hybrid learning objective that integrates dual-stream reconstruction loss with prototype-based contrastive discrimination, enabling the joint optimization of pattern restoration and discriminative representation learning. Our extensive experiments on the UCF-Crime, UBI-Fights, and UBnormal datasets demonstrate state-of-the-art performance, achieving AUC scores of 98.9%, 94.7%, and 82.9%, respectively. The explicit spatiotemporal encoding scheme further improves temporal alignment accuracy by 2.4%, contributing to enhanced anomaly localization and overall detection accuracy. Additionally, the proposed framework achieves a 14.3% reduction in FLOPs and demonstrates 18.7% faster convergence during training, highlighting its practical value for real-world deployment. Our optimized window-shift attention mechanism also reduces computational complexity, making MT-CMVAD a robust and efficient solution for safety-critical video understanding tasks.

Person re-identification (ReID) refers to the task of retrieving target persons from image libraries captured by various distinct cameras. Over the years, person ReID has yielded favorable recognition outcomes under typical visible light conditions, yet there remains considerable scope for enhancement in challenging conditions. The challenges and research gaps include the following: multi-modal data fusion, semi-supervised and unsupervised learning, domain adaptation, ReID in 3D space, fast ReID, decentralized learning, and end-to-end systems. The main problems to be solved, which are the occlusion problem, viewpoint problem, illumination problem, background problem, resolution problem, openness problem, etc., remain challenges. For the first time, this paper uses person ReID in special scenarios as a basis for classification to categorize and analyze the related research in recent years. Starting from the perspectives of person ReID methods and research directions, we explore the current research status in special scenarios. In addition, this work conducts a detailed experimental comparison of person ReID methods employing deep learning, encompassing both system development and comparative methodologies. In addition, we offer a prospective analysis of forthcoming research approaches in person ReID and address unresolved concerns within the field.

Defect detection is a critical task in industrial manufacturing, playing a vital role in achieving automation, improving product quality, and ensuring operational safety. Traditional methods, however, face considerable limitations in terms of accuracy and efficiency. To address these challenges, we propose DCA-YOLO, a lightweight model for steel surface defect detection optimized based on task-specific knowledge. Specifically, our model incorporates a Dynamic Snake Convolution (DSConv) module to capture subtle linear features in challenging defect categories, a context-guided module to leverage contextual information for detecting clustered defects, and an Adaptive Spatial Feature Fusion (ASFF) mechanism to efficiently merge features across scales. The experimental results demonstrate that even with a nanoscale architecture (4.3 million parameters and 9.4 GFLOPs), the enhanced model exhibits marked improvements in detection accuracy and robustness, with mAP50 increasing by 4.6% and mAP50-95 by 7.7%. These findings not only offer a better solution for steel surface defect detection, but also provide new theoretical insights and practical experience for the advancement of industrial inspection technologies. In the future, DCA-YOLO is expected to be applied across a wider range of industrial detection scenarios, further driving progress in the field.

Lightweight visual models are crucial for industrial defect detection tasks. Traditional methods and even some lightweight detectors often struggle with the trade-off between high computational demands and insufficient accuracy. To overcome these issues, this study introduces YOLO-MAD, an innovative model optimized through a multi-scale geometric structure feature extraction and fusion scheme. YOLO-MAD integrates three key modules: AKConv for robust geometric feature extraction, BiFPN to facilitate effective multi-scale feature integration, and Detect_DyHead for dynamic optimization of detection capabilities. Empirical evaluations demonstrate significant performance improvements: YOLO-MAD achieves a 5.4% mAP increase on the NEU-DET dataset and a 4.8% mAP increase on the GC10-DET dataset. Crucially, this is achieved under a moderate computational load (9.4 GFLOPs), outperforming several prominent lightweight models in detection accuracy while maintaining comparable efficiency. The model also shows enhanced recognition performance for most defect categories. This work presents a pioneering approach that balances lightweight design with high detection performance by efficiently leveraging multi-scale geometric feature extraction and fusion, offering a new paradigm for industrial defect detection.