Benchmarking Recurrent Event-Based Object Detection for Industrial Multi-Class Recognition on MTEvent

• URL: http://arxiv.org/abs/2603.21787v1
• Date: Mon, 23 Mar 2026 10:28:54 GMT
• Title: Benchmarking Recurrent Event-Based Object Detection for Industrial Multi-Class Recognition on MTEvent
• Abstract summary: Event cameras are attractive for industrial robotics because they provide high temporal resolution, high dynamic range, and reduced motion blur.<n>Most event-based object detection studies focus on outdoor driving scenarios or limited class settings.<n>We benchmark recurrent ReYv8s on MTEvent for industrial multi-class recognition.
• Score: 1.7188280334580195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras are attractive for industrial robotics because they provide high temporal resolution, high dynamic range, and reduced motion blur. However, most event-based object detection studies focus on outdoor driving scenarios or limited class settings. In this work, we benchmark recurrent ReYOLOv8s on MTEvent for industrial multi-class recognition and use a non-recurrent YOLOv8s variant as a baseline to analyze the effect of temporal memory. On the MTEvent validation split, the best scratch recurrent model (C21) reaches 0.285 mAP50, corresponding to a 9.6% relative improvement over the nonrecurrent YOLOv8s baseline (0.260). Event-domain pretraining has a stronger effect: GEN1-initialized fine-tuning yields the best overall result of 0.329 mAP50 at clip length 21, and unlike scratch training, GEN1-pretrained models improve consistently with clip length. PEDRo initialization drops to 0.251, indicating that mismatched source-domain pretraining can be less effective than training from scratch. Persistent failure modes are dominated by class imbalance and human-object interaction. Overall, we position this work as a focused benchmarking and analysis study of recurrent event-based detection in industrial environments.

Related papers

• IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning [57.16513298507272]
  We propose IADD-TR, a unified framework combining Intervention-Aware Dynamics Decoupling (IADD) and Targeted Regularization (TR)<n>IADD factorizes transitions into an action-intervention stage and an action-free natural evolution stage, using a zero-action anchor to resolve the non-uniqueness of this two-stage factorization for robust generalization.<n>For policy learning, we derive TR from the efficient influence function of a replay-state policy-gradient functional.
  arXiv  Detail & Related papers  (2026-08-11T08:20:02Z)
• Evaluating the Effect of Frame Rate in Sequence-Based Classification of Autism-Related Self-Stimulatory Hand Idiosyncrasies [1.091319386322552]
  This study addresses two challenges in automated detection of autism-related self-stimulatory behaviors from video.<n>It identifies the optimal sequence-based neural network architecture and temporal sampling rate.<n>It also characterizes data augmentation strategies for training on small behavioral datasets.
  arXiv  Detail & Related papers  (2026-07-08T22:22:37Z)
• Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environments [1.1263731027301402]
  This study introduces a hybrid deep learning model for intrusion detection in Industrial IoT (IIoT) systems.<n>It combines ResNet-1D, BiGRU, and Multi-Head Attention (MHA) for effective spatial-temporal feature extraction and attention-based feature weighting.<n>The model achieved 98.71% accuracy, a loss of 0.0417%, and low inference latency (0.0001 sec /instance), demonstrating strong real-time capability.
  arXiv  Detail & Related papers  (2026-04-07T21:32:03Z)
• Enhancing Continual Learning for Software Vulnerability Prediction: Addressing Catastrophic Forgetting via Hybrid-Confidence-Aware Selective Replay for Temporal LLM Fine-Tuning [43.24339861841546]
  This paper investigates fine-tuning of a decoder-style language model (phi/phi-2 with LoRA) on a CVE-linked dataset.<n>We evaluate eight continual learning strategies, including window-only and cumulative training, replay-based baselines and regularisation-based variants.<n>Hybrid-CASR reduces training time per window by about 17 percent compared to the baseline, whereas cumulative training delivers only a minor F1 increase (0.661) at a 15.9-fold computational cost.
  arXiv  Detail & Related papers  (2026-02-27T09:13:23Z)
• Reshaping Action Error Distributions for Reliable Vision-Language-Action Models [69.38615670891038]
  In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies.<n>We focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training.<n>We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures.
  arXiv  Detail & Related papers  (2026-02-04T05:37:09Z)
• Rep Smarter, Not Harder: AI Hypertrophy Coaching with Wearable Sensors and Edge Neural Networks [0.9939775124582318]
  This paper introduces a novel system for real-time feedback on near-failure states during resistance exercise using only a single wrist-mounted Inertial Measurement Unit (IMU)<n>A ResNet-based model segments repetitions from the 6-axis IMU data in real-time and a classification model identifies exercise windows corresponding to near-failure states.<n> Deployment on a Raspberry Pi 5 yielded an average inference latency of 112 ms, and on an iPhone 16 yielded 23.5 ms, confirming the feasibility for edge computation.
  arXiv  Detail & Related papers  (2025-12-05T01:49:47Z)
• Reinforcement Learning for Reasoning in Large Language Models with One Training Example [117.86853102104256]
  We show that reinforcement learning with verifiable reward using one training example (1-shot RLVR) is effective in incentivizing the math reasoning capabilities of large language models (LLMs)<n>We identify some interesting phenomena during 1-shot RLVR, including cross-category generalization, increased frequency of self-reflection, and sustained test performance improvement.
  arXiv  Detail & Related papers  (2025-04-29T09:24:30Z)
• Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone [6.515830463304737]
  This work quantitatively evaluates the performance of event-based vision systems (EVS) against conventional RGB-based models for action prediction in collision avoidance on an FPGA accelerator.<n>The EVS model achieves a significantly higher effective frame rate (1 kHz) and lower temporal (-20 ms) and spatial prediction errors (-20 mm) compared to the RGB-based model.<n>These results underscore the advantages of event-based vision for real-time collision avoidance and demonstrate its potential for deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-04-14T16:51:10Z)
• Video Prediction Transformers without Recurrence or Convolution [65.93130697098658]
  We propose PredFormer, a framework entirely based on Gated Transformers.<n>We provide a comprehensive analysis of 3D Attention in the context of video prediction.<n>The significant improvements in both accuracy and efficiency highlight the potential of PredFormer.
  arXiv  Detail & Related papers  (2024-10-07T03:52:06Z)
• Fall Detection for Industrial Setups Using YOLOv8 Variants [0.0]
  The YOLOv8m model, consisting of 25.9 million parameters and 79.1 GFLOPs, demonstrated a respectable balance between computational efficiency and detection performance. Although the YOLOv8l and YOLOv8x models presented higher precision and recall, their higher computational demands and model size make them less suitable for resource-constrained environments.
  arXiv  Detail & Related papers  (2024-08-08T17:24:54Z)
• PUMA: margin-based data pruning [51.12154122266251]
  We focus on data pruning, where some training samples are removed based on the distance to the model classification boundary (i.e., margin) We propose PUMA, a new data pruning strategy that computes the margin using DeepFool. We show that PUMA can be used on top of the current state-of-the-art methodology in robustness, and it is able to significantly improve the model performance unlike the existing data pruning strategies.
  arXiv  Detail & Related papers  (2024-05-10T08:02:20Z)
• Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness [52.9493817508055]
  We propose Pre-trained Model Guided Adversarial Fine-Tuning (PMG-AFT) to enhance the model's zero-shot adversarial robustness. Our approach consistently improves clean accuracy by an average of 8.72%.
  arXiv  Detail & Related papers  (2024-01-09T04:33:03Z)
• Domain Adaptation with Adversarial Training on Penultimate Activations [82.9977759320565]
  Enhancing model prediction confidence on unlabeled target data is an important objective in Unsupervised Domain Adaptation (UDA) We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features.
  arXiv  Detail & Related papers  (2022-08-26T19:50:46Z)
• Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training [70.2914594796002]
  We propose Dynamic R-CNN to adjust the label assignment criteria and the shape of regression loss function. Our method improves upon ResNet-50-FPN baseline with 1.9% AP and 5.5% AP$_90$ on the MS dataset with no extra overhead.
  arXiv  Detail & Related papers  (2020-04-13T15:20:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.