FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

Authors: Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Erkhembayar Ganbold, Ming-Ching Chang, Ping-Yang Chen, Byambaa Dorj, Hamad Al Jassmi, Ganzorig Batnasan, Fady Alnajjar, Mohammed Abduljabbar, Fang-Pang Lin, Jun-Wei Hsieh

We provide detailed information on the new FishEye8K road object detection dataset. The dataset consists of 8,000 annotated images with 157K bounding boxes of five object classes. The figure displays sample images from each of the 18 cameras with wide-angle fisheye views. These cameras offer new possibilities for extensive coverage.

Results of YOLOv7-E6E model on the input size 1280x1280.

concatedVideo_Trim2.mp4

Classes (Color)

• - Bus
• - Bike
• - Car
• - Pedestrian
• - Truck

Dataset

• Click to download the Fisheye8K dataset
• To download the whole dataset, choose Fisheye8K_all_including_train&test_update_2024Jan Update.zip
• Click on "Explore" and "Go to resource"

• Note: If you downloaded the older version Fisheye8K_all_including_train&test_1.zip before Jan 28, MS COCO (train.json and test.json) labels should be replaced with these updated versions Click here to download correct MS COCO labels

Paper

• Click to download the paper

Train and Test

Experimental results

We evaluated using default yolo test.py

• conf_thres=0.5
• iou_thres=0.5

Model	Version	Input Size	Precision	Recall	mAP0.5	mAP.5-.95	f1-score	APS	APM	APL	Inference[ms]
YOLOv5	YOLOv5l6	1280	0.7929	0.4076	0.6139	0.4098	0.535	0.1299	0.434	0.6665	22.7
	YOLOv5x6	1280	0.8224	0.4313	0.6387	0.4268	0.5588	0.133	0.452	0.6925	43.9
YOLOR	YOLOR-W6	1280	0.7871	0.4718	0.6466	0.4442	0.5899	0.1325	0.4707	0.6901	16.4
	YOLOR-P6	1280	0.8019	0.4937	0.6632	0.4406	0.6111	0.1419	0.4805	0.7216	13.4
YOLOv7	YOLOv7-D6	1280	0.7803	0.4111	0.3977	0.2633	0.5197	0.1261	0.4462	0.6777	26.4
	YOLOv7-E6E	1280	0.8005	0.5252	0.5081	0.3265	0.6294	0.1684	0.5019	0.6927	29.8
YOLOv7	YOLOv7	640	0.7917	0.4373	0.4235	0.2473	0.5453	0.1108	0.4438	0.6804	4.3
	YOLOv7-X	640	0.7402	0.4888	0.4674	0.2919	0.5794	0.1332	0.4605	0.7212	6.7
YOLOv8	YOLOv8l	640	0.7835	0.3877	0.612	0.4012	0.5187	0.1038	0.4043	0.6577	8.5
	YOLOv8x	640	0.8418	0.3665	0.6146	0.4029	0.5106	0.0997	0.4147	0.7083	13.4

Citation

@InProceedings{Gochoo_2023_CVPR,
    author    = {Gochoo, Munkhjargal and Otgonbold, Munkh-Erdene and Ganbold, Erkhembayar and Hsieh, Jun-Wei and Chang, Ming-Ching and Chen, Ping-Yang and Dorj, Byambaa and Al Jassmi, Hamad and Batnasan, Ganzorig and Alnajjar, Fady and Abduljabbar, Mohammed and Lin, Fang-Pang},
    title     = {FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
    pages     = {5304-5312}
}