This paper provides a comprehensive survey of recent advancements in deep learning techniques for generic object detection. Object detection, a fundamental and challenging task in computer vision, aims to locate instances of predefined categories in natural images. Deep learning has revolutionized this field by enabling the automatic learning of complex feature representations from data, leading to significant improvements in performance. The survey covers over 300 research contributions, addressing various aspects such as detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. It highlights the evolution from handcrafted features to deep convolutional neural networks (DCNNs) and discusses the challenges and progress in generic object detection. The paper also identifies promising future research directions, emphasizing the need for efficient, accurate, and scalable systems that can handle a wide range of object categories and real-world scenarios.This paper provides a comprehensive survey of recent advancements in deep learning techniques for generic object detection. Object detection, a fundamental and challenging task in computer vision, aims to locate instances of predefined categories in natural images. Deep learning has revolutionized this field by enabling the automatic learning of complex feature representations from data, leading to significant improvements in performance. The survey covers over 300 research contributions, addressing various aspects such as detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. It highlights the evolution from handcrafted features to deep convolutional neural networks (DCNNs) and discusses the challenges and progress in generic object detection. The paper also identifies promising future research directions, emphasizing the need for efficient, accurate, and scalable systems that can handle a wide range of object categories and real-world scenarios.