The recent advancements in computer vision and deep learning have significantly enhanced the capabilities of object detection and image classification, particularly in specialized and challenging domains. Innovations in attention mechanisms and feature extraction techniques are driving improvements in model performance, enabling more accurate and efficient detection of small, subtle, or spatially dispersed features. These advancements are particularly notable in applications involving cultural heritage preservation, fire detection, drone-based surveillance, and microscopic analysis of materials. The integration of prior knowledge and context-guided modules with deep learning models is also proving to be a powerful strategy for enhancing detection accuracy and robustness. Lightweight and efficient models are being developed to address the computational constraints of real-time applications, such as drone-based target detection and edge device deployment. These developments not only advance the technical capabilities of computer vision but also contribute to critical areas like environmental conservation, public safety, and cultural heritage protection.

Noteworthy papers include one that introduces a novel attention mechanism for image classification, significantly improving accuracy across multiple datasets, and another that presents an enhanced YOLO model for fire detection, demonstrating superior performance in real-world scenarios.