Yolov7-Based Pedestrian Detection: Performance Evaluation and Real-World Applications

Introduction

Object detection, a fundamental task in computer vision, has numerous applications. Recent advances in deep learning have significantly improved object detection, with Yolov7 (You Only Look Once Version 7) emerging as a highly effective and popular algorithm. This paper focuses on pedestrian detection using Yolov7 and evaluates its performance in real-world settings.

Methodology

Dataset

The COCO dataset was used for training, validation, and testing. It comprises a vast collection of images featuring pedestrians in diverse scenarios. The training set contains 118,287 images with 328,000 pedestrians, while the validation set includes 5,000 images with 12,000 pedestrians, and the testing set consists of 40,000 images with 107,000 pedestrians.

Model

We utilized ResNet as the feature extractor, incorporating additional convolutional and connection layers to enhance detection accuracy. The output layer was adjusted to suit the pedestrian detection task. Multi-scale detection techniques were implemented to improve accuracy in detecting pedestrians across different scales. Further optimization for pedestrian detection involved adding convolutional and pooling layers to the Yolov7 algorithm.

Training

Model training employed the cross-entropy loss function and stochastic gradient descent. The learning rate was set at 0.001, with a batch size of 64. The model underwent 100 epochs of training, with the best model selection based on validation set performance.

Evaluation

Evaluation of Yolov7 on the testing set was performed using mean average precision (mAP) as the metric. mAP was calculated for various intersection over union (IoU) thresholds ranging from 0.5 to 0.95. The recall rate and detection time were also evaluated to compare Yolov7's performance with other state-of-the-art pedestrian detection methods.

Results

Our experiments demonstrate that Yolov7 achieved high detection accuracy, reaching an mAP of 86.7% at an IoU threshold of 0.5. The recall rate was 95.2%, indicating effective detection of most pedestrians in the testing set. The detection time was 27.4ms per image, signifying Yolov7's fast detection speed.

Real-world applications involved deploying Yolov7 in crowded locations such as subway stations and squares. Experimental results confirmed Yolov7's ability to accurately and quickly detect pedestrians in such settings. However, in extreme cases like pedestrian occlusion and low light conditions, Yolov7 exhibited some false negatives, missing certain pedestrians.

Conclusion

This paper presented a pedestrian detection method based on Yolov7, evaluating its performance using the COCO dataset. Our experiments revealed Yolov7's high detection accuracy and fast detection speed. However, it has limitations in challenging scenarios. Future work will focus on exploring more effective techniques to improve pedestrian detection performance in such cases.