Pith. sign in

REVIEW 5 cited by

Object Detection in 20 Years: A Survey

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1905.05055 v3 pith:T6OXBSCH submitted 2019-05-13 cs.CV

Zhengxia Zou (1) , Keyan Chen (1) , Zhenwei Shi (1) , Yuhong Guo (2) , Jieping Ye (3) ((1) Beihang University , (2) Carleton University , (3) Alibaba Group) This is my paper
classification cs.CV
keywords detectionobjectcomputervisionevolutionfieldfundamentalrecent
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past two decades, we have seen a rapid technological evolution of object detection and its profound impact on the entire computer vision field. If we consider today's object detection technique as a revolution driven by deep learning, then back in the 1990s, we would see the ingenious thinking and long-term perspective design of early computer vision. This paper extensively reviews this fast-moving research field in the light of technical evolution, spanning over a quarter-century's time (from the 1990s to 2022). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed-up techniques, and the recent state-of-the-art detection methods.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 560 citations worldwide. Full citation record

  1. Unsupervised Detection of Entry and Exit Regions from Vehicle Trajectories for Camera-Agnostic Turning Movement Counts

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Clustering start and end points of vehicle trajectories yields persistent entry/exit polygons that classify turning movements at ~3% median error across 25 uncalibrated cameras.

  2. OpenGuide: Assistive Object Retrieval in Indoor Spaces for Individuals with Visual Impairments

    cs.RO 2025-09 conditional novelty 6.0 of 10

    OpenGuide combines vision-language value maps, frontier exploration, and POMDP planning to locate multiple objects in unfamiliar indoor spaces, reaching about 55% success in simulation and 54% in real-world trials.

  3. Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A U-Net trained on CAD layouts and flawed wafer photos can generate defect-free synthetic wafer images that serve as a template for automated defect detection in InP multi-project wafer manufacturing.

  4. Object Recognition Datasets and Challenges: A Review

    cs.CV 2025-07 conditional novelty 2.0 of 10

    A review paper that compiles statistics and descriptions of over 160 object recognition datasets, their associated challenges, and evaluation metrics.

  5. Adaptive Object Detection with ESRGAN-Enhanced Resolution & Faster R-CNN

    cs.CV 2025-06 reject novelty 2.0 of 10

    The paper applies the known ESRGAN super-resolution step before Faster R-CNN detection and asserts, without supporting evidence, that this improves detection accuracy.

Pith tools