AI-Powered Traffic Violation Detection System

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@UzeeTech

Your models are trained but the accuracy isn't where it needs to be for reliable violation detection. That's a classic fine-tuning problem — the architecture is likely fine, but the training recipe needs work for your specific violation classes. I'd start by profiling the current models to find where accuracy drops across the seven violation types, then apply targeted data augmentation and hyperparameter tuning. For helmet, seatbelt, and mobile usage, I'd fine-tune on augmented data capturing the edge cases your models miss, validating with per-class precision-recall so you see exactly what improved. I built Jyotish AI, a live AI astrology app on Android and web, where model accuracy directly impacts user trust. What architecture are the current models, and do you have labeled data for all seven types? I can start with an evaluation and share a fine-tuning plan before changing anything.

@abhilashr53

I can deliver 'AI-Powered Traffic Violation Detection System' end to end — clean architecture, tests, and deployment. I'll start with a short plan, share progress daily, and hand over production-ready code. Could you confirm your timeline and must-have features so I can lock the scope?

@Wonitaf

Hi there, I totally get the importance of having accurate models for your traffic violation detection system. With my background in PyTorch and computer vision, I have successfully trained models that significantly improved detection accuracy in similar projects. I noticed you specifically need fine-tuning for various violations like helmet detection and number plate recognition. I’m all about creating clean, professional, and user-friendly solutions that integrate seamlessly into existing systems. I specialize in optimizing models for real-time detection, ensuring they are both efficient and effective. You can count on me for speedy communication and a fast turnaround. Let me know if you are available for a quick chat! Regards, Wonita

@Webslinger01

Worked on a PyTorch-based traffic violation detection system using computer vision for vehicle monitoring and automated violation identification. Focused on model integration, image/video processing, and reliable detection results. Your models already detect but miss too much — that's a training and data problem, not an architecture problem, so fine-tuning is the right call before any rebuild. Here's what I bring to this: → I've shipped real PyTorch/YOLO detection on edge hardware — an on-device shelf vision system and a smart vending fridge that identifies products via YOLOv8 within the door-close window. → Fine-tuning traffic models: I'd start by auditing your dataset for class imbalance and label noise, since helmet, triple-riding and seatbelt classes usually fail on bad annotations, not the model. → Full stack in-house — PyTorch training, dataset work, plus firmware/PCB/edge deployment if you later need this running on a Jetson or Pi at the junction. My approach: → First, run your current models against a held-out set and pull per-class precision/recall so we know exactly which violations are failing. → Then targeted fine-tuning: augmentation, class balancing, anchor/confidence tuning, and hard-negative mining per class. → Finally, hand back the retrained weights with a short report showing the accuracy gain per violation type.

@Daniyal153

Hi, I’m highly focused on computer vision, deep learning, and AI model fine-tuning, and this project is directly aligned with the kind of work I’ve been building and working on. I have hands-on experience with computer vision systems, real-time detection, deep learning models, and fine-tuning models for specific use cases. I’ve worked on projects involving face recognition, attendance systems , cctv based town security systems, object detection, and AI-based automation that do the work themselves, so I understand the challenges around detection accuracy, false positives, dataset quality, and model optimization. For your project, I’m specifically interested in working with your existing PyTorch models and improving their performance rather than rebuilding the entire system. I can analyze the current training setup, dataset/annotations, model performance, and fine-tune the models specifically for better accuracy across helmet detection, wrong-lane detection, accidents, triple riding, seatbelt detection, mobile usage, and number-plate recognition. I’m new to this platform, but I’m not new to AI/Computer Vision. My profile here is new because I’m starting to take on projects through this platform, while my technical background and project experience have been built outside of it. I’d be happy to review your existing models and training setup and start from there Best regards Daniyal AI Engineer & Full-Stack Developer

@dushyant317

Hi, we have strong expertise in PyTorch and Computer Vision model training and can fine-tune your existing traffic violation models to improve detection accuracy. We’ll work directly with your current models and dataset, optimize training parameters, evaluate precision/recall and relevant metrics, and provide the improved trained models without unnecessary additional development. We can also share relevant computer vision work samples.

@nabiullax86

I read the brief: you already have the traffic-violation project and trained PyTorch models, but accuracy needs improvement across helmet, lane, accident, triple-riding, seatbelt, mobile-use and number-plate cases. Our AI delivery team can focus on the fine-tuning rather than rebuilding the system. Week 1: review the repository, dataset/labels and current metrics; reproduce a baseline; inspect class imbalance and false positives; fine-tune with suitable augmentation/loss settings; then validate against a held-out set and share an error breakdown plus the next recommended iteration. One question: do you have the current model, labelled validation set and per-violation metrics available, and which violation is currently weakest? If the data/code are ready, message me and we can quickly align on the baseline and first experiment.

@hexabitzprojects

Hi, I can fine-tune your **existing PyTorch traffic-violation models** specifically to improve detection accuracy, without changing the overall project or taking on unrelated development work. I have **16 years of AI, computer vision and embedded R&D experience**, including completed **AI Dashcam, ANPR, OCR, YOLO and real-time machine-vision projects**. I can work directly with your existing models and training pipeline to improve: * Helmet detection * Wrong-lane detection * Accident detection * Triple riding * Seatbelt detection * Mobile-phone usage * Number-plate recognition My approach would focus on **dataset quality, annotation issues, class imbalance, augmentation, transfer learning, hyperparameter tuning, confidence/IoU thresholds and validation analysis** to identify the actual sources of missed detections and false positives. I can provide before/after validation metrics and training results so the improvement is measurable rather than subjective. Relevant experience includes **AI Dashcam and ANPR systems using YOLO/computer vision**, including vehicle/number-plate detection and real-time video processing. I can work with your existing **PyTorch models, datasets and code** and focus purely on model fine-tuning and accuracy improvement. Best regards, Subhash Suman PyTorch | Computer Vision | YOLO | ANPR | AI Dashcam | Model Training

@guptakishan805

As an accomplished Full Stack Developer with over 5 years of specializing in various technologies, including those that are directly applicable to your AI-Powered Traffic Violation Detection System. One of my key specialties is in Real-Time Systems and AI so I'm confident in my ability to help fine-tune your PyTorch models specifically and improve model accuracy. My solid foundation in Computer Vision and extensive experience in Deep Learning, Image Processing, and Image Recognition makes me the ideal choice for this project. Plus, my knowledge on ML will allow me to incorporate the relevant traffic regulations into model training. Aside from technical skills, I value clear communication, transparency, and attention to detail throughout the development process. I always work closely with clients to not only meet their expectations but offer improvements and suggestions where needed. With an aim to build long-term relationships through quality work and dependable support, I'm excited about the possibility of working together! To further illustrate my capabilities, I'll provide a few relevant work samples during our discussion. Let's connect!