Object detection
Related papers: 20
About
Object detection is a fundamental computer vision technique that enables machines to automatically identify and localize objects within images, video streams, or 3D sensor data by drawing bounding boxes around detected instances and assigning them category labels. In robotics and AI, it serves as a critical perception layer, powering applications ranging from autonomous driving and warehouse automation to agricultural harvesting robots and manipulation systems. Approaches span classical convolutional neural networks to real-time single-stage detectors like YOLO and 3D volumetric methods such as VoxelNet and VoxNet that process LiDAR or RGB-D point clouds. These techniques allow robots to identify pedestrians, obstacles, graspable objects, and dynamic scene elements necessary for safe navigation and interaction. Object detection matters because it bridges raw sensor input and higher-level reasoning—without reliable detection, downstream tasks like pose estimation, grasping, tracking, and SLAM cannot function effectively. Standardized benchmarks such as KITTI have accelerated progress by providing rigorous evaluation frameworks, making modern detectors accurate and fast enough for deployment in demanding real-world robotic environments.
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Top Cited Papers
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun
Citations: 14348 • 2012
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
Yin Zhou, Oncel Tuzel
Citations: 4542 • 2018
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Daniel Maturana, Sebastian Scherer
Citations: 3579 • 2015
SECOND: Sparsely Embedded Convolutional Detection
Yan Yan, Yuxing Mao, Bo Li
Citations: 3212 • 2018
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
Juan Terven, Diana‐Margarita Córdova‐Esparza, Julio-Alejandro Romero-González
Citations: 2518 • 2023
Deep Learning for 3D Point Clouds: A Survey
Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, Mohammed Bennamoun
Citations: 2225 • 2020
Convolutional networks and applications in vision
Yann LeCun, Koray Kavukcuoglu, Clément Farabet
Citations: 2163 • 2010
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
Xiang Yu, Tanner Schmidt, Venkatraman Narayanan, Dieter Fox
Citations: 2088 • 2018
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Saurabh Gupta, Ross Girshick, Pablo Arbeláez, Jitendra Malik
Citations: 1531 • 2014
Pedestrian detection: A benchmark
Piotr Dollár, Christian Wojek, Bernt Schiele, Pietro Perona
Citations: 1339 • 2009
A large-scale hierarchical multi-view RGB-D object dataset
Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox
Citations: 1322 • 2011
Monocular Pedestrian Detection: Survey and Experiments
Markus Enzweiler, Dariu M. Gavrila
Citations: 1226 • 2008
Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
Stefan Hinterstoißer, Vincent Lepetit, Slobodan Ilić, Stefan M. Holzer, Gary Bradski, Kurt Konolige, Nassir Navab
Citations: 1190 • 2013
DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes
Citations: 1152 • 2018
DeepFruits: A Fruit Detection System Using Deep Neural Networks
Inkyu Sa, Zongyuan Ge, Feras Dayoub, Ben Upcroft, Tristán Pérez, Chris McCool
Citations: 1079 • 2016
Real-time grasp detection using convolutional neural networks
Joseph Redmon, Anelia Angelova
Citations: 912 • 2015
Learning to Track: Online Multi-object Tracking by Decision Making
Xiang Yu, Alexandre Alahi, Silvio Savarese
Citations: 716 • 2015
A Review on YOLOv8 and Its Advancements
Mupparaju Sohan, Thotakura Sai Ram, Ch. Venkata Rami Reddy
Citations: 687 • 2024
Bayesian approach to extended object and cluster tracking using random matrices
Wolfgang Koch
Citations: 666 • 2008
Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
Yu Yang, Kailiang Zhang, Yang Li, Dongxing Zhang
Citations: 626 • 2019