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rollingcoconut
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drone-detection-assignment3

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video
string
frame_index
int64
image
image
num_detections
int64
detections
string
drone_video_1
0
1
[{"bbox": [839.67, 173.83, 919.57, 229.07], "confidence": 0.8177}]
drone_video_1
1
1
[{"bbox": [848.82, 177.65, 921.14, 233.83], "confidence": 0.8272}]
drone_video_1
2
1
[{"bbox": [839.35, 180.63, 923.53, 238.95], "confidence": 0.8217}]
drone_video_1
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[{"bbox": [838.66, 183.22, 917.36, 242.3], "confidence": 0.8055}]
drone_video_1
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1
[{"bbox": [837.13, 185.74, 910.53, 243.64], "confidence": 0.8057}]
drone_video_1
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1
[{"bbox": [838.46, 191.13, 911.39, 248.28], "confidence": 0.822}]
drone_video_1
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1
[{"bbox": [833.15, 191.27, 917.81, 249.94], "confidence": 0.8165}]
drone_video_1
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1
[{"bbox": [833.64, 194.88, 909.66, 251.7], "confidence": 0.8076}]
drone_video_1
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1
[{"bbox": [831.44, 198.53, 907.11, 251.57], "confidence": 0.8078}]
drone_video_1
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1
[{"bbox": [829.16, 196.89, 911.42, 254.27], "confidence": 0.8058}]
drone_video_1
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drone_video_1
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drone_video_1
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1
[{"bbox": [829.4, 193.62, 913.58, 255.36], "confidence": 0.8058}]
drone_video_1
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drone_video_1
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[{"bbox": [840.83, 198.8, 910.36, 256.66], "confidence": 0.8125}]
drone_video_1
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[{"bbox": [836.0, 201.16, 913.06, 257.65], "confidence": 0.8092}]
drone_video_1
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[{"bbox": [834.3, 200.76, 917.66, 257.68], "confidence": 0.8111}]
drone_video_1
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[{"bbox": [841.44, 201.84, 917.25, 258.53], "confidence": 0.828}]
drone_video_1
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[{"bbox": [845.1, 201.8, 918.23, 259.05], "confidence": 0.826}]
drone_video_1
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[{"bbox": [847.28, 203.21, 922.02, 260.48], "confidence": 0.8154}]
drone_video_1
20
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[{"bbox": [839.88, 203.01, 926.38, 261.89], "confidence": 0.8163}]
drone_video_1
21
1
[{"bbox": [841.14, 206.52, 923.15, 261.35], "confidence": 0.8172}]
drone_video_1
22
1
[{"bbox": [848.81, 201.3, 918.27, 261.45], "confidence": 0.829}]
drone_video_1
23
1
[{"bbox": [838.48, 202.65, 919.1, 261.27], "confidence": 0.8252}]
drone_video_1
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[{"bbox": [833.49, 202.62, 921.28, 260.71], "confidence": 0.8191}]
drone_video_1
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[{"bbox": [838.89, 200.39, 916.34, 258.99], "confidence": 0.8151}]
drone_video_1
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drone_video_1
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drone_video_1
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[{"bbox": [839.27, 197.42, 915.46, 256.26], "confidence": 0.8191}]
drone_video_1
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drone_video_1
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[{"bbox": [838.26, 197.86, 917.95, 254.76], "confidence": 0.8088}]
drone_video_1
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1
[{"bbox": [830.68, 197.77, 917.8, 254.38], "confidence": 0.8042}]
drone_video_1
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1
[{"bbox": [831.34, 190.75, 917.94, 250.72], "confidence": 0.8128}]
drone_video_1
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[{"bbox": [831.07, 191.28, 915.73, 250.86], "confidence": 0.8147}]
drone_video_1
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drone_video_1
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drone_video_1
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drone_video_1
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drone_video_1
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1
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drone_video_1
39
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drone_video_1
40
1
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drone_video_1
41
1
[{"bbox": [831.14, 187.84, 916.08, 246.03], "confidence": 0.8225}]
drone_video_1
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1
[{"bbox": [830.56, 186.99, 918.72, 246.45], "confidence": 0.8074}]
drone_video_1
43
1
[{"bbox": [838.36, 188.27, 914.2, 244.22], "confidence": 0.8205}]
drone_video_1
44
1
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drone_video_1
45
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drone_video_1
46
1
[{"bbox": [836.91, 188.65, 915.82, 245.43], "confidence": 0.8298}]
drone_video_1
47
1
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drone_video_1
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drone_video_1
49
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drone_video_1
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drone_video_1
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drone_video_1
52
1
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drone_video_1
53
1
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drone_video_1
54
1
[{"bbox": [839.04, 183.39, 921.78, 241.04], "confidence": 0.8117}]
drone_video_1
55
1
[{"bbox": [842.02, 181.7, 921.52, 240.13], "confidence": 0.8065}]
drone_video_1
56
1
[{"bbox": [842.94, 181.43, 921.46, 238.56], "confidence": 0.8058}]
drone_video_1
57
1
[{"bbox": [843.06, 183.17, 920.89, 238.14], "confidence": 0.8148}]
drone_video_1
58
1
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drone_video_1
59
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drone_video_1
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drone_video_1
61
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[{"bbox": [832.83, 177.99, 920.85, 234.93], "confidence": 0.8207}]
drone_video_1
62
1
[{"bbox": [839.67, 174.9, 914.49, 232.49], "confidence": 0.8192}]
drone_video_1
63
1
[{"bbox": [839.18, 173.41, 917.27, 233.67], "confidence": 0.8283}]
drone_video_1
64
1
[{"bbox": [833.24, 172.64, 915.13, 231.86], "confidence": 0.8093}]
drone_video_1
65
1
[{"bbox": [833.49, 173.38, 918.13, 230.84], "confidence": 0.8164}]
drone_video_1
66
1
[{"bbox": [835.65, 170.92, 916.86, 231.09], "confidence": 0.804}]
drone_video_1
67
1
[{"bbox": [833.91, 170.02, 911.92, 228.74], "confidence": 0.7963}]
drone_video_1
68
1
[{"bbox": [833.97, 171.47, 918.47, 231.41], "confidence": 0.8089}]
drone_video_1
69
1
[{"bbox": [837.82, 173.74, 924.5, 230.28], "confidence": 0.8003}]
drone_video_1
70
1
[{"bbox": [838.0, 171.98, 921.66, 227.85], "confidence": 0.8104}]
drone_video_1
71
1
[{"bbox": [843.29, 170.95, 921.03, 227.43], "confidence": 0.8113}]
drone_video_1
72
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[{"bbox": [842.69, 171.9, 922.41, 229.77], "confidence": 0.8081}]
drone_video_1
73
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drone_video_1
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drone_video_1
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drone_video_1
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[{"bbox": [843.51, 171.09, 923.51, 231.16], "confidence": 0.808}]
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drone_video_1
93
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94
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drone_video_1
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drone_video_1
98
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drone_video_1
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[{"bbox": [840.97, 172.96, 917.94, 233.58], "confidence": 0.8179}]
End of preview. Expand in Data Studio

UAV Drone Detection and Tracking

Detect and track drones in video using YOLO fine-tuned detector, Kalman Filter w/ tuned noise parameters.

Source Videos

Video URL
drone_video_1 YouTube - DhmZ6W1UAv4
drone_video_2 YouTube - YrydHPwRelI

Training Dataset

Detector

  • Model: YOLOv8n fine-tuned on Seraphim Drone Detection Dataset
  • Training: 15 epochs, 640x640, batch 16
  • Classes: 1 (drone)
  • Performance: Precision 0.93, Recall 0.863
  • Frame extraction: 5 fps via ffmpeg

Schema

detections — Frame-Level Drone Detections

One row per video frame that contains at least one drone detection. Confidence threshold >= 0.5.

Column Type Description
video string Video name (drone_video_1, drone_video_2)
frame_index int Frame number within the video (0-indexed at 5 fps)
image bytes The video frame image (JPEG)
num_detections int Number of drones detected in this frame
detections string (JSON list) List of detections with bbox and confidence

Example detections value:

[{"bbox": [839.67, 173.83, 919.57, 229.07], "confidence": 0.8177}]

NOTE: [x_min, y_min, x_max, y_max] pixel coordinates.

Detection Summary

Video Total Frames Frames with Detections Detection Rate
drone_video_1 828 551 66.5%
drone_video_2 2,580 178 6.9%
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Number of rows:
729
Total file size:
220 MB