Integration with IoT devices for automatic road condition

The gap

Evidence profile

Stated in the future work and recommendations and cells future research sections of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 4 representative gaps

• IoT-Enabled Speed and Accident Detection Platform Using Deep Learning and Multi-Object Tracking (2026) · Open Access Journal on Engineering Applications · doi

  Several directions for future enhancement are identified: 1. Accident Detection: Incorporating LSTM or transformer-based temporal sequence models to detect abnor- mal vehicle behavior patterns—including sudden stops, abrupt lane changes, and collision trajectories—to enable proactive accident detection and automated emergency dispatch. 2. Multi-Modal IoT Sensing: Integrating IoT sensors (accelerometers, GPS modules, vibration sensors) with the video analytics pipeline for multi-modal accident confirmation, improving both detection reliability and geo-location precision. 58 Open Access Journal on Engineering Applications DOI:10.64886/oajea.0102.006 3. Cloud-Scale Deployment: Extending the MQTT IoT layer to support multi-camera deployments connected to centralized cloud dashboards, enabling city-wide real-time traffic monitoring, congestion prediction, and automated traffic signal control. 4. Nighttime and Adverse Weather Robustness: Incorporating infrared or thermal camera inputs, along with domain adaptation techniques, to maintain detection and tracking accuracy under low illumination, rain, and fog conditions. 5. Edge Deployment: Porting the YOLOv8 inference to edge AI hardware (NVIDIA Jetson, Google Coral) to enable fully self-contained, low-latency, and low-power deployment at roadside locations without reliance on central GPU servers. 6. License Plate Recognition: Integrating Optical Character Recognition (OCR) for license plate reading to enable automated violation record creation and linkage to traffic authority databases.

  generalstated in future workevidence 5/5
  Keywords: detection accident enable automated multi deployment traffic incorporating modal integrating sensors cloud camera edge license
• Multimodal Computer Vision for Rapid Disaster Damage Analysis and Victim Detection using Deep Learning (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The current implementation establishes a functional baseline for hybrid disaster scene analysis. Several directions are identified for future development. Domain-Specific Model Fine-Tuning: Training YOLOv8 or YOLO11 variants on annotated disaster- scene datasets including fire, smoke, rubble, and collapsed structures would replace the HSV and edge density modules with more precise neural alternatives. Public datasets such as AIDER (Aerial Image Dataset for Emergency Response) and the Foggia fire detection dataset could serve as training foundations. Quantized Neural Networks: Applying post- training quantization (INT8) to the YOLOv8n-seg model using tools such as ONNX Runtime or TensorRT (if future deployments include GPU hardware) would further reduce inference latency, potentially enabling 30 FPS victim tracking on current CPU hardware. Temporal Object Tracking: a tracker such as SORT or lightweight multi-object identity ByteTrack would enable consistent victim tracking across frames, providing trajectory information useful for rescue coordination.

  generalstated in future workevidence 5/5
  Keywords: training tracking current disaster scene future model yolov datasets fire neural dataset hardware victim object
• GNSS and multi-modal data integration methods for landslide displacement monitoring: a review (2026) · Geohazards & Remediation · doi

  Future developments involve cloud–edge co-process- ing and low-power wide-area networks (LPWANs) for IoT. To scale up monitoring in mountainous regions, it is recommended to adopt edge intelligence for low-latency preprocessing (time sync, denoising, outlier removal, and  baseline-shift mitigation) and on-device anomaly screening, while cloud platforms handle model train- ing, multi-sensor fusion, and data archiving. In addition, constructing digital twins of landslides based on physics- informed and data-assimilative models can bridge the gap between observational and computational agents, providing an intelligent, interpretable module for mecha- nistic explanation and operational decision-making.

  generalstated in recommendationsevidence 5/5
  Keywords: cloud edge future developments involve process power wide area networks lpwans scale monitoring mountainous regions
• Pot Hole Detection Using Yolov8 (2026) · International Journal for Research in Applied Science and Engineering Technology · doi

  Integration with IoT devices for automatic road condition monitoring and maintenance alerts. - Cloud-based road monitoring for large-scale road monitoring and centralized data storage. - Drone-based inspection systems for monitoring highways, rural roads, and inaccessible areas.

  generalstated in cells future researchevidence 5/5
  Keywords: integration iot devices automatic road condition monitoring maintenance

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