Transportation perception

SmartParking

Which object detector best supports the mixed visual demands of a smart parking environment?

Presented at IEEE ICTC 2022 in Jeju Island, South Korea

SmartParking repository TraPedesVeh dataset IEEE paper

Why I built it

How do Faster R-CNN and SSD detector families trade detection quality against training speed when they are evaluated on the same multi-class parking dataset?

SmartParking is a computer-vision study of joint vehicle, pedestrian, cyclist, bicycle, bus, and traffic-sign detection. Unlike work that treats each object family as a separate problem, this project evaluated the combined perception task required around a parking facility.

The study also introduced TraPedesVeh, a labeled mini-dataset assembled to address the limited availability of images containing the multiple road users and signs that a parking system must understand at the same time.

Smart parking object-detection system from dataset preparation to traffic-scene deployment

The complete system connects the TraPedesVeh dataset to preprocessing, feature extraction, classification, evaluation, and real-traffic testing for vehicles, pedestrians, and traffic signs.

How the system works

TraPedesVeh dataset

Images containing vehicles, pedestrians, cyclists, bicycles, buses, and traffic signs were collected, annotated, normalized, and converted into a common detection format.

Six detector comparison

The study evaluated SSD and Faster R-CNN variants with MobileNet, ResNet, and Inception-ResNet backbones under a shared computational setting.

Multi-metric evaluation

Detection accuracy, average precision, average recall, training time, classification loss, localization loss, and total loss exposed the speed-accuracy tradeoff.

91.5%best average detection accuracy

6detector configurations evaluated

6reported object classes

5.636 sfastest reported training time

What this work contributes

From a research question to an inspectable system.

A unified evaluation of vehicle, pedestrian, cyclist, bicycle, bus, and traffic-sign detection.
TraPedesVeh, a labeled mini-dataset for intelligent transportation research.
Faster R-CNN with Inception-ResNet achieved the highest average detection accuracy at 91.5 percent.
SSD-MobileNet at 320 x 320 trained fastest, documenting the practical speed-accuracy tradeoff.
A reproducible baseline for perception around smart parking environments.

TraPedesVeh dataset construction and annotation process — The dataset pipeline moves from class-keyword selection and image retrieval through screening, annotation, TFRecord conversion, and label-map creation.

How I read the results

What the evidence means

The highest-accuracy model was also the slowest to train, while the fastest SSD configuration sacrificed substantial detection quality. That tension is central to smart-parking deployment: the best model depends on whether the system prioritizes detection fidelity, limited hardware, retraining speed, or real-time response.

Current scope

What this study validates

SmartParking is a perception and detector-benchmarking project. It is separate from PANDA, which forecasts future parking occupancy and synchronizes predictions with a Cesium digital twin.

Inside the system

More evidence from the research.

SmartParking detection result for pedestrians and a vehicle — A qualitative result showing joint pedestrian and vehicle detections in a street scene. These mixed scenes reflect the perception demands near parking facilities.

SmartParking detection result for pedestrians, cyclists, and bicycles — A multi-object result with pedestrians, cyclists, and bicycles detected in the same urban scene.

SmartParking comparison result for vehicles and pedestrians — An additional detector output used to compare localization and class confidence across the evaluated model families.

SmartParking result detecting pedestrians, vehicles, and a traffic sign — This dense urban scene tests simultaneous localization of pedestrians, vehicles, and a traffic sign, including partially overlapping people and small distant objects.

SmartParking result detecting pedestrians and bicycles in a crowded street — A crowded street result demonstrates multi-class perception with pedestrians and bicycles at different scales, orientations, and levels of occlusion.