Yolov7-Based Pedestrian Detection: Performance and Practical Applications

Introduction

Pedestrian detection is a crucial task in computer vision, with numerous practical applications, including surveillance, autonomous driving, and robotics. Yolov7 (You Only Look Once Version 7) stands as a state-of-the-art object detection algorithm, capable of identifying objects within an image through a single forward pass through a neural network. This paper delves into the utilization of Yolov7 for pedestrian detection, examining its performance and analyzing its suitability within real-world scenarios.

Methodology

The training of our pedestrian detection model was conducted using the COCO dataset, a comprehensive collection encompassing a vast number of images across various scenes, contributing to the robustness of our algorithm. ResNet was employed as the feature extractor, enhanced by the addition of multiple convolutional and connection layers to elevate detection accuracy. During the training phase, we adopted the cross-entropy loss function and gradient descent algorithm for model optimization.

We tailored Yolov7 for pedestrian detection by incorporating new convolutional and pooling layers into the network structure and adjusting the output layer. Multi-scale detection technology was introduced to ensure precise pedestrian detection across diverse scales. The algorithm's performance was evaluated using metrics such as precision, recall, and F1-score.

Results

Our experiments revealed that our algorithm exhibited high accuracy and recall rates in pedestrian detection. The precision, recall, and F1-score achieved by our algorithm were 0.93, 0.89, and 0.91, respectively. Further testing was conducted in crowded environments like subway stations and squares, demonstrating the algorithm's ability to detect pedestrians swiftly and accurately, even amidst dense crowds.

However, our algorithm did encounter limitations. In certain extreme situations, such as pedestrian occlusion and low-light conditions, some false positives and false negatives were observed. We conducted an analysis of the impact of different hyperparameters on the algorithm's performance, discovering that adjustments to the threshold and anchor size could significantly enhance its effectiveness.

Conclusion

In conclusion, Yolov7 emerges as an efficient and accurate pedestrian detection algorithm, characterized by its robustness and practical applicability. However, real-world deployments necessitate optimization and adjustments tailored to specific scenes to maximize detection effectiveness. Our algorithm provides a foundation for future research in pedestrian detection and holds promise for diverse applications across various fields.

Experimental Data and Image

(Please note: This is a sample and requires actual data and images for a real research paper. The content below is illustrative.)

Table 1: Performance Metrics of Yolov7-Based Pedestrian Detection Model

| Metric | Value | |---|---| | Precision | 0.93 | | Recall | 0.89 | | F1-score | 0.91 |

Figure 1: Example of Pedestrian Detection in a Crowded Scene

(Insert image here showing pedestrian detection results in a crowded scene)

Figure 2: Example of Pedestrian Detection in Low-Light Conditions

(Insert image here showing pedestrian detection results in low-light conditions)

Discussion

(Include a discussion section analyzing the experimental results, comparing them with other methods, and addressing limitations and future work.)