Lightweight Network for Real-Time Object Detection in Fisheye Cameras

Fig. 8. The detection effects under different conditions. (a)‒(c) Original images; (a1)‒(c1) detection results of EfficientDet; (a2)‒(c2) detection results of PGDS-YOLOv8s; (a3)‒(c3) detection results of YOLOv8m; (a4)‒(c4) detection results of Fisheye-YOLOv8

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Fig. 9. Front camera and side camera detection results.(a)‒(b) Original images; (a1)‒(b1) detection results of EfficientDet; (a2)‒(b2) detection results of PGDS-YOLOv8s; (a3)‒(b3) detection results of YOLOv8m; (a4)‒(b4) detection results of Fisheye-YOLOv8

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Fig. 10. Detection effects of LOAF dataset. (a)‒(d) Original images; (a1)‒(d1) detection results of YOLOv8m; (a2)‒(d2) detection results of Fisheye-YOLOv8

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Fig. 11. Detection effects of VisDrone2019 dataset. (a)‒(d) Original image; (a1)‒(d1) detection results of YOLOv8m; (a2)‒(d2) detection results of Fisheye-YOLOv8

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Table 1. The impact of each module on different indicators

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Table 1. The impact of each module on different indicators

Method	P /%	R /%	mAP /%	Params /10⁶	GFLOPs /10⁹	FPS
YOLOv8m	79.8	34.6	58.3	25.8	78.7	89
YOLOv8m+Faster-EMA	79.5	34.4	58.3	20.4	68.2	95
YOLOv8m+Faster-EMA+RFA-BiFPN	79.9	35.8	59.9	12.2	61.2	101
YOLOv8m+Faster-EMA+RFA-BiFPN+G-LHead	82.2	34.7	59.8	10.0	48.7	109
YOLOv8m+Faster-EMA+RFA-BiFPN+G-LHead+LAMP	83.1	36.3	60.5	5.3	23.4	118

Table 2. Influences of each module on different detection objects

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Table 2. Influences of each module on different detection objects

Model	mAP /%
Model	bus	bike	car	pedestrian	truck
YOLOv8m	69.5	58.3	69.7	36.5	44.9
YOLOv8m+Faster-EMA	68.1	57.9	70.5	37.1	45.5
YOLOv8m+Faster-EMA+RFA-BiFPN	67.8	58.8	71.1	41.8	59.6
YOLOv8m+Faster-EMA+RFA-BiFPN+G-LHead	66.3	59.1	71.2	43.1	59.2
YOLOv8m+Faster-EMA+RFA-BiFPN+G-LHead+LAMP	70.1	59.8	70.6	43.2	58.8

Table 3. Performance comparison of different algorithms on Fisheye8K data set

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Table 3. Performance comparison of different algorithms on Fisheye8K data set

Model	P /%	R /%	mAP /%	Params /10⁶	GFLOPs /10⁹	FPS
Faster-RCNN	65.2	27.1	24.8	136.9	370.2	27
EfficientDet	69.4	33.3	44.5	12.0	25.1	29
SSD	67.8	32.1	37.3	26.3	62.7	43
YOLOv5s	81.5	25.9	54.1	9.1	23.8	123
YOLOv7	78.9	41.5	39.9	36.5	103.2	129
YOLOv8m	79.8	34.6	58.3	25.8	78.7	89
FisheyeDet	78.2	33.5	55.3	16.3	44.2	108
PGDS-YOLOv8s	82.3	35.7	60.2	10.8	28.5	115
Fisheye-YOLOv8	83.1	36.3	60.5	5.3	23.4	118

Table 4. Performance comparison of different algorithms on WoodScape datasets

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Table 4. Performance comparison of different algorithms on WoodScape datasets

Model	P /%	R /%	mAP /%
Faster-RCNN	63.7	25.3	23.1
EfficientDet	67.9	38.5	42.5
SSD	68.6	40.6	38.6
YOLOv5s	78.3	35.2	56.6
YOLOv7	75.7	46.2	42.4
YOLOv8m	76.5	41.2	58.5
FisheyeDet	76.8	43.1	56.8
PGDS-YOLOv8s	78.2	42.3	59.2
Fisheye-YOLOv8	78.5	44.2	59.7

Table 5. Generalization studies on LOAF datasets
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Table 5. Generalization studies on LOAF datasets
Model P /% R /% mAP /% Params /10⁶ GFLOPs /10⁹ FPS
YOLOv8m 77.5 40.5 61.2 25.8 78.7 95
Fisheye-YOLOv8 77.3 45.3 62.3 5.3 23.4 127

Table 6. Generalization study on VisDrone2019 dataset
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Table 6. Generalization study on VisDrone2019 dataset
Model P /% R /% mAP /% Params /10⁶ GFLOPs /10⁹ FPS
YOLOv8m 51.2 40.5 41.1 25.8 78.7 98
Fisheye-YOLOv8 53.4 41.0 42.6 5.3 23.4 132

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Xinlei Wang, Chenxu Liao, Shuo Wang, Ruilin Xiao. Lightweight Network for Real-Time Object Detection in Fisheye Cameras[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0237012

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