Bird Detection Algorithm in Natural Scenes Based on Improved YOLOv3

Fig. 5. Comparison of experimental results of different algorithms. (a) Images seriously affected by light; (b) images affected by fog; (c) images taken normally

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Table 1. AP of different algorithms for different birds

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Table 1. AP of different algorithms for different birds

Bird class	SSD300	YOLOv3	Faster RCNN	YOLOBIRDS
baiqueling0	71.85	87.78	88.54	91.15
bailu1	73.78	87.65	90.12	88.57
haiou2	87.82	88.97	86.32	82.12
heizuiou3	69.49	84.32	83.54	92.85
huiqiongniao4	90.56	91.12	92.39	91.32
luzi5	63.89	87.75	89.43	86.48
shanmaque6	61.43	78.57	82.45	79.58
xiaopiti7	79.33	85.67	88.23	91.82
zhuomuniao8	60.64	75.34	83.44	87.67
dae9	53.51	76.93	79.14	79.64

Table 2. Overall model performance comparison of different algorithms

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Table 2. Overall model performance comparison of different algorithms

Method	mAP /%	Speed /（frame·s^-1）	Access inventory /MB	Number of parameters	FLOPs /10⁹
SSD300	71.23	45.64	107.51
YOLOv3	84.41	27.23	254.03	61760674	60.68
Faster RCNN	86.36	12.52	588.59
YOLOBIRDS	87.12	32.67	132.72	12425917	24.22

Table 3. Comparison between YOLOBIRDS and YOLOv3 series

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Table 3. Comparison between YOLOBIRDS and YOLOv3 series

Method

mAP /%

Speed /（frame·s^-1）

YOLOv3（origin，darknet53）

YOLOv3（VGG19）

YOLOv3（Resnet50）

YOLOv3（Inceptionv4）

YOLOv3（DenseNet）

YOLOv3（SENet）

YOLOv3（DualPathNet）

YOLOBIRDS

84.41

78.69

79.58

82.89

84.51

82.54

86.46

87.12

27.23

38.15

24.59

26.25

27.97

30.18

31.65

32.67

Table 4. Comparison of mAP of different algorithms under different IoU thresholds
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Table 4. Comparison of mAP of different algorithms under different IoU thresholds
Method R_IoU=0.5 R_IoU =0.6 R_IoU＝0.7
SSD300
YOLOv3
Faster RCNN
YOLOBIRDS
71.23
84.41
86.36
87.12
62.39
72.33
77.38
76.27
55.72
67.32
69.33
70.34

Table 5. Index parameters of different algorithms under different confidence thresholds

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Table 5. Index parameters of different algorithms under different confidence thresholds

Method	Precision /%			Recall /%			F1_Score /%
Method	0.25	0.45	0.65	0.25	0.45	0.65	0.25	0.45	0.65
SSD300	69.86	72.76	81.83	65.09	73.28	83.29	67.39	73.02	82.55
YOLOv3	62.47	74.87	82.28	69.12	76.27	84.91	65.63	75.56	83.57
Faster RCNN	63.39	78.63	83.09	66.93	78.92	86.53	65.11	78.77	84.78
YOLOBIRDS	66.34	79.37	85.13	69.42	78.20	85.91	67.85	78.78	85.52

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Ziying Song, Kuihe Yang, Yu Zhang. Bird Detection Algorithm in Natural Scenes Based on Improved YOLOv3[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815013

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