# Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks Source: [Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:44:30.600Z Authors: Shivang Agarwal, Jean Ogier Du Terrail, Fr\\'ed\\'eric Jurie Published: 2018-09-10T09:09:12.000Z ## Abstract Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., 'car', 'plane', etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()