Object detection is a crucial task in computer vision that aims to identify and localize objects in images or videos. The recent advancements in deep learning and Convolutional Neural Networks (CNNs) have significantly improved the performance of object detection techniques. This paper presents a comprehensive study of object detection techniques in unconstrained environments, including various challenges, datasets, and state-of-the-art approaches. Additionally, we present a comparative analysis of the methods and highlight their strengths and weaknesses. Finally, we provide some future research directions to further improve object detection in unconstrained environments.

翻译：目标检测是计算机视觉中的重要任务，旨在识别和定位图像或视频中的对象。深度学习和卷积神经网络（CNN）的最新发展显著提高了目标检测技术的性能。本文在非受限环境下，包括各种挑战、数据集和最先进的方法，对目标检测技术进行了全面的研究。另外，我们进行了方法的比较分析，并突出了其优势和劣势。最后，我们提供了一些未来的研究方向，以进一步改进非受限环境下的目标检测。