基于深度卷积神经网络的目标检测研究综述

Object detection, which is a fundamental visual recognition problem in computer vision, has been extensively studied in the past few decades and has become one of the popular research areas in the world. The aim of object detection is to accurately locate specific objects in a given image and assign a corresponding label to each object. In recent years, Deep Convolutional Neural Networks (DCNN) have been used in a series of developments in object detection and image classification owing to their powerful capabilities of feature learning and transfer learning.It has garnered considerable attention in the field of computer vision for object detection. Therefore, the method of applying CNN in target detection to obtain better performance is an important topic for research.First, we reviewed and introduced several types of classic object detection algorithms.Next, we considered the generation process of the deep learning algorithm as a starting point, analyzed the technical ideas and key problems of DCNN in the application of target detection, and provided a comprehensive overview of various target detection methods in a systematic manner. Finally, in view of the major challenges in target detection and deep learning algorithms, we provided future development scope and direction to promote the study of target detection using deep learning.