deep_learning_object_detection
A paper list of object detection using deep learning.
A curated reading list of deep learning object detection research papers from 2014 to 2020, with a visual timeline and a benchmark performance table comparing major methods.
This repository is a curated reading list of academic research papers on object detection using deep learning (the branch of AI that teaches computers to recognize things in images and video). It is not a tool you install and run. It is a reference resource maintained by one researcher for others who want to follow the history and progress of this field.
Object detection is the task of not just recognizing what is in an image, but also drawing boxes around each thing (a cat, a person, a car) and labeling them. The papers in this list cover major methods developed from 2014 onward, including well-known approaches like R-CNN, YOLO, SSD, and RetinaNet, as well as many others from top research conferences such as CVPR, ICCV, ECCV, and NeurIPS.
The README includes a visual timeline diagram showing how detection methods evolved year by year, and a large performance comparison table. The table shows how different detectors score on standard benchmark datasets (VOC and COCO), giving researchers a quick way to compare methods. Each entry in the paper list includes a link to the original paper and, where available, links to official or community code implementations.
The list was actively maintained from 2018 through September 2020, with monthly or quarterly updates adding newly published work. After that date, no further updates are shown in the log.
This repository is useful for researchers, students, or developers who want a structured entry point into the object detection literature, or who need to survey what methods existed up to 2020. There is no installation or code to run. The full README is longer than what was shown.
Where it fits
- Survey the history of deep learning object detection from R-CNN through YOLO and other major methods up to 2020.
- Compare accuracy scores across different detection methods using the VOC and COCO benchmark table in one place.
- Find links to original papers and code implementations for a specific object detection approach you want to study or reproduce.