Latest Object Detection Research Papers

The newest Object Detection papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Object Detection so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals
  Paul Fergus, Philip Stephens, Russell A. Hill, Lee Oliver et al. · arXiv · Jun 9, 2026

  Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does no…

• Using the YOLOv12 Model for Verifying the Correct Color Sequence of Wires in Network Cables (Patch Cords) on the Production Line
  Amin Doroodchi, Danial Soleimany · arXiv · Jun 9, 2026

  In the production process of network cables, ensuring the correct color sequence of wire pairs inside the standard connector plays a critical role in the final performance of the cable, as any misplacement or color-ordering error can lead t…

• Analyzing Training-Free Corruption Detection for Object Detection Datasets
  Christian Sieberichs, Simon Geerkens, Thomas Waschulzik, Viswanathan Ramesh et al. · arXiv · Jun 9, 2026

  Annotation errors are widespread in computer vision datasets and can significantly degrade the performance of systems trained on them, particularly in complex tasks such as object detection. Several approaches exist to identify annotation e…

• ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
  Debojyoti Biswas, Xianbiao Hu · arXiv · Jun 8, 2026

  3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scena…

• Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation
  Lucas Görnhardt, Timo Bartels, Niklas Schwarz, Tim Fingscheidt · arXiv · Jun 8, 2026

  Conventional one-hot encodings often yield poorly calibrated models, being overconfident under attack, and letting entropy-based detection algorithms fail. Previous image classification works have demonstrated that Hadamard-coded output rep…

• ContextShift: A Controlled Benchmark for Context Dependence in Object Detection
  Dan Zlotnikov, Alex Lazarovich, Ohad Ben-Shahar · arXiv · Jun 8, 2026

  Modern object detectors achieve strong performance on standard benchmarks, yet their robustness to contextual variation remains insufficiently understood. Prior evaluations largely rely on aggregate metrics such as AP on uncontrolled distri…

• RT-SDGOD: Real-Time Single-Domain Generalized Object Detection
  Yupeng Zhang, Fangzhuo Gao, Ruize Han, Wei Feng et al. · arXiv · Jun 8, 2026

  In real-world deployment under strict real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading detectors. Single-Domain Generalized Object Detection aims to mitigate this issue, yet ex…

• Taming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification
  Cornelius Schröder, Žygimantas Marcinkus, Markus Lienkamp · arXiv · Jun 8, 2026

  Reliable motion classification is critical for autonomous driving, as false dynamic predictions of static objects can cascade into unnecessary planner interventions. Unstable bounding box predictions can lead to spurious velocity estimates …

• Proposal Refinement for Few-Shot Object Detection
  Yuan Zeng, Bin Song, Jie Guo, Yuwen Chen · arXiv · Jun 8, 2026

  Few-shot object detection has gained widely attention in recent years. Some excellent algorithms have been proposed to handle this task. However, most of these algorithms rely on the performance of few-shot classification. Unlike previous a…

• Differences in Detection: Explainability Where it Matters
  Johannes Theodoridis, Johannes Maucher, Andreas Schilling · arXiv · Jun 5, 2026

  We propose Differences in Detection (DnD), an intuitive method to compare two object detection models. Based on the same matching algorithm, it complements the standard metrics of mean Average Precision ($mAP$) and TIDE error analysis with …

• CL-CLIP: CLIP-Based Continual Learning Framework with Cost-Volume Category Decoupling for Object Detection
  Zihan Liu, Yuguang Yang, Shengjie Su, Jianing Pang et al. · arXiv · Jun 5, 2026

  Continual Object Detection (COD) requires a detector to acquire new categories over time while preserving previously learned ones. This goal is closely related to open-vocabulary detection, since both settings require reasoning over categor…

• Unveiling the Unknown: Open Vocabulary Object Detection with Scene Graphs
  Yi Chen, Yinghao Lu, Zhehao Li, Chenchen Yan et al. · arXiv · Jun 4, 2026

  Open-vocabulary object detection seeks to identify novel object categories that were not part of the training data. Many knowledge distillation-based approaches have shown promising performance by transferring knowledge from pre-trained vis…

• Next-Generation Parallel Decoder for LPDR: Architectural Optimization and Class-Balanced GAN-Augmentation
  Shawaiz Obaid, Nida Chandio, Neha Jamil, Muhammad Khuram Shahzad · arXiv · Jun 4, 2026

  Real-Time License Plate Detection and Recognition (LPDR) forms the backbone of modern smart cities. Although the YOLOV5-PDLPR model substantially improved system efficiency through a parallel decoder approach, its performance is still affec…

• Cycle Consistency in Video Object-Centric Learning
  Rongzhen Zhao, Zhiyuan Li, Ruonan Wei, Juho Kannala et al. · arXiv · May 28, 2026

  Self-supervised video Object-Centric Learning (OCL) aims to discover distinct objects and associate them across time, whereas self-supervised Multi-Object Tracking (MOT) focuses on associating pre-defined object detections or segmentations.…

• LV-OSD: Language-Vision-Complementary Open-Set Object Detection
  Yupeng Zhang, Ruize Han, Wei Feng, Song Wang et al. · arXiv · May 27, 2026

  Object detection is an important task in computer vision, which aims to detect the objects of interest. through the given category list or query images. In this work, we propose a new problem of language-visual-complementary open-set object…

• Pixel-Level Pavement Distress Assessment Using Instance Segmentation
  Logan Dewick, Bibesh Pyakurel, Kong Pheng Yang, Nazim Choudhury et al. · arXiv · May 25, 2026

  Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localization of thin, branching, and irregular cracks to achieve the geometric precision necessary for …

• SAM3-Assisted Training of Lightweight YOLO Models for Precision Pig Farming
  Marcos Vinicius Mendes Faria, Thiago Borges Pereira, Isabella C. F. S. Condotta, Thiago Meireles Paixão et al. · arXiv · May 25, 2026

  Deep learning-based object detection has revolutionized Precision Livestock Farming (PLF), yet a critical barrier remains: high-performance Foundation Models (such as SAM 3) are too computationally intensive for edge deployment, while light…

• Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection
  Liuyang Wang, Feitian Zhang · arXiv · May 21, 2026

  Object detection from Unmanned Aerial Vehicles (UAVs) is challenged by severe ego-motion, camera jitter, and large scale variations. While modern detectors perform well on static images, their direct application to UAV video often fails, pa…

• Detection of Virus and Small Cell Patches in Foci Images Using Switchable Convolution and Feature Pyramid Networks
  Amrita Singh, Snehasis Mukherjee · arXiv · May 21, 2026

  Accurate detection and counting of virus patches in focus-forming unit (FFU) images, also known as foci images, are important for quantifying viral infection and analyzing cellular structures. This task is challenging because biomedical tar…

• EgoExoMem: Cross-View Memory Reasoning over Synchronized Egocentric and Exocentric Videos
  Ruiping Liu, Junwei Zheng, Yufan Chen, Di Wen et al. · arXiv · May 18, 2026

  Egocentric memory is widely used in embodied intelligence, but it may be insufficient for comprehensive spatial-temporal reasoning. Inspired by human recall from both field and observer perspectives, we introduce EgoExoMem, the first benchm…

• CrossView Suite: Harnessing Cross-view Spatial Intelligence of MLLMs with Dataset, Model and Benchmark
  Wei Wang, Yuqian Yuan, Tianwei Lin, Wenqiao Zhang et al. · arXiv · May 18, 2026

  Spatial intelligence requires multimodal large language models (MLLMs) to move beyond single-view perception and reason consistently about objects, visibility, geometry, and interactions across multiple viewpoints. However, progress in cros…

• Characterizing the visual representation of objects from the child's view
  Jane Yang, Tarun Sepuri, Alvin Wei Ming Tan, Khai Loong Aw et al. · arXiv · May 14, 2026

  Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning process look like? We analyzed first-person videos of young children's visual experience at…

• 6D Pose Estimation via Keypoint Heatmap Regression with RGB-D Residual Neural Networks
  Ismail Aljosevic, Amir Masoud Almasi, Ana Parovic, Ashkan Shafiei · arXiv · May 8, 2026

  In this paper, we propose a modular framework for 6D pose estimation based on keypoint heatmap regression. Our approach combines YOLOv10m for object detection with a ResNet18-based network that predicts 2D heatmaps from RGB images. Keypoint…

• AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes
  Xiaochen Huang, Honggang Chen, Weicheng Zhang, Xiaobo Dai et al. · arXiv · May 7, 2026

  In multimedia application scenarios, images captured under low-illumination conditions often lead to lower accuracy in visual perception tasks compared to those taken in well-lit environments. To tackle this challenge, we propose AMIEOD, an…

• A unified Benchmark for Multi-Frame Image Restoration under Severe Refractive Warping
  Maxim V. Shugaev, Md Reshad Ul Hoque, Bridget Kennedy, Joseph T. Riley et al. · arXiv · May 6, 2026

  Video sequence capturing through refractive dynamic media, such as a turbulent air or water surface, often suffer from severe geometric distortions and temporal instability. While recent advances address mild atmospheric turbulence, no exis…

• Physical Adversarial Clothing Evades Visible-Thermal Detectors via Non-Overlapping RGB-T Pattern
  Xiaopei Zhu, Guanning Zeng, Zhanhao Hu, Jun Zhu et al. · arXiv · May 6, 2026

  Visible-thermal (RGB-T) object detection is a crucial technology for applications such as autonomous driving, where multimodal fusion enhances performance in challenging conditions like low light. However, the security of RGB-T detectors, p…

• Reference-based Category Discovery: Unsupervised Object Detection with Category Awareness
  Yichen Li, Qiankun Liu, Ying Fu · arXiv · May 6, 2026

  Traditional one-shot detection methods have addressed the closed-set problem in object detection, but the high cost of data annotation remains a critical challenge. General unsupervised methods generate pseudo boxes without category labels,…

• Reward-Guided Semantic Evolution for Test-time Adaptive Object Detection
  Lihua Zhou, Mao Ye, Xiatian Zhu, Nianxin Li et al. · arXiv · May 6, 2026

  Open-vocabulary object detection with vision-language models (VLMs) such as Grounding DINO suffers from performance degradation under test-time distribution shifts, primarily due to semantic misalignment between text embeddings and shifted …

• StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning
  Xiaowen Sun, Matthias Kerzel, Mengdi Li, Xufeng Zhao et al. · arXiv · May 5, 2026

  Vision-language models (VLMs) have shown remarkable performance in various robotic tasks, as they can perceive visual information and understand natural language instructions. However, when applied to robotics, VLMs remain subject to a fund…

• The Detector Teaches Itself: Lightweight Self-Supervised Adaptation for Open-Vocabulary Object Detection
  Yazhe Wan, Changjae Oh · arXiv · May 5, 2026

  Open-vocabulary object detection aims to recognize objects from an open set of categories, which leverages vision-language models (VLMs) pre-trained on large-scale image-text data. The cooperative paradigm combines an object detector with a…