The Effect of Light Intensity, Camera Pixel Quality, Camera Distance, and Object Altitude on Detection Accuracy in a Real-Time Drone Surveillance System Using YOLOv5

Abstract

This research evaluates the performance of the drone detection system based on YOLOv5 in a variety of environmental conditions. The four main variables under test were drone height, camera type, light intensity, and camera-to-object distance. Thirty-six different scenarios were used with three different camera types (1080p, 2K, and Canon 600D). The height of the drones varied from 1 to 14 meters, and the variations in illumination ranged from 0 to 46 lux. Results showed consistent YOLOv5 performance with an average accuracy of 60%, precision of 62%, recall of 58%, F1-score of 60%, and IoU of 75%. ANOVA revealed that light intensity, camera distance, and drone height all had a significant impact on detection accuracy (p < 0.05), but camera type was not statistically significant. The best results were obtained under the following conditions: high light levels (>40 lux), camera distances <10 m, and drone altitudes between 6 and 9 m. These findings demonstrate the importance of environmental setup in improving the performance of object detection systems based on deep learning. This research helps design a more reliable and adaptable drone detection system for real-world applications. This work provides practical guidelines for implementing deep learning-based aerial surveillance and highlights optimal operational parameters for YOLOv5 systems.