Abstract
We present an enhanced YOLOv8 real time vehicle detection and classification framework, for estimating carbon emissions in urban environments. The system enhances YOLOv8 architecture to detect, segment, and track vehicles from live traffic video streams. Once a vehicle is localized, a dedicated deep learning-based identification module is employed to recognize license plates and classify vehicle types. Since YOLOv8 lacks the built-in capacity for fine grained recognition tasks such as reading license plates or determining vehicle attributes beyond class labels, our framework incorporates a hybrid pipeline where each detected vehicle is tracked and its bounding box is cropped and passed to a deep Optical Character Recognition (OCR) module. This OCR system, composed of multiple convolutional neural network (CNN) layers, is trained specifically for character-level detection and license plate decoding under varied conditions such as motion blur, occlusion, and diverse font styles. Additionally, the recognized plate information is validated using a real time API that cross references with an external vehicle registration database to ensure accurate classification and emission estimation. This multi-stage approach enables precise, automated calculation of per vehicle carbon emissions. Extensive evaluation was conducted using a diverse vehicle dataset enriched with segmentation masks and annotated license plates. The YOLOv8 detector achieved a mean Average Precision (mAP@0.5) of approximately 71% for bounding boxes and 70% for segmentation masks. Character level OCR accuracy reached up to 99% with the best performing CNN model. These results affirm the feasibility of combining real time object detection with deep OCR for practical deployment in smart transportation systems, offering a scalable solution for automated, vehicle specific carbon emission monitoring.
Abstract (translated)
我们提出了一种增强版的YOLOv8实时车辆检测和分类框架，用于在城市环境中估算碳排放。该系统改进了YOLOv8架构，能够从实时交通视频流中检测、分割并追踪车辆。一旦定位到一辆车，将使用一个专门基于深度学习的身份识别模块来识别车牌号码，并对车辆类型进行分类。由于YOLOv8本身缺乏读取车牌或确定超出类别标签的车辆属性等精细化任务的能力，我们的框架采用了一种混合流水线方法，在该方法中，每辆检测到的车辆都会被追踪并且其边界框会被裁剪并传递给一个深度光学字符识别（OCR）模块。这个OCR系统由多个卷积神经网络(CNN)层组成，并且特别针对在运动模糊、遮挡和不同字体样式等条件下进行字符级检测和车牌解码进行了训练。 此外，通过实时API验证所识读的车牌信息，该API与外部车辆注册数据库交叉参考以确保准确分类和排放估算。这种多阶段方法能够实现每辆车碳排放的精确自动化计算。使用一组多样化的带有分割掩模和注释车牌的数据集进行广泛的评估后发现，YOLOv8检测器在边界框方面达到了约71%的平均精度(mAP@0.5)，而在分割掩模方面则为70%。字符级OCR准确率最高可达99%，这表明结合实时目标检测与深度OCR在智能交通系统中的实际部署是可行的，提供了一种针对自动化、车辆特定碳排放监测的可扩展解决方案。
URL
https://arxiv.org/abs/2506.18924
