IVP-YOLOv5: an intelligent vehicle-pedestrian detection method based on YOLOv5s

Figures & data

Figure 1. Structures of conventional convolution and Ghost module.

Figure 2. Ghost-BottleNeck module.

Figure 3. Improved yolov5s network structure.

Figure 4. Slicing Aided Hyper Inference strategies.

Table 1. Configuration of the experimental environment of the computing platform.

				Development	Programming
Systems	CPU	GPU	Memory	Framework	Languages
Ubuntu	Gaold6148	Tesla	16G	PaddlePaddle	Python 3.7
16.04	2.40GHz	V100		2.2.2

Table 2. Classification results for datasets containing pedestrians.

	Training set	Test set
Datasets	(pedestrian)	(pedestrian)
PASCAL VOC	8527	2132
BDD100K	17994	4499
Nuscenes	7276	1819

Table 3. Statistics on the proportion of pedestrian objects in different scales in the dataset.

		small-scale	medium-scale	large-scale
		pedestrian	pedestrian	pedestrian
Datasets	image size	[pixels < 32 × 32]	[32 × 32 ≤ pixels ≤ 96 × 96]	[pixels ≥ 96 × 96]
PASCAL VOC	–	18.1%	40.3%	41.6%
BDD100K	1280 × 720	28.3%	54.8%	16.9%
Nuscenes	1600 × 900	34.4%	52.2%	13.4%

Figure 5. Ablation experiment results on PASCAL VOC dataset.

Table 4. The PASCAL VOC dataset test results.

Dataset	type	AP/%	GFLOPs	Parameters/M	Speed/ms
PASCAL VOC	v5s	87.9	16	7.1	33
	v5s + GhB	88.7	10.5	4.9	31
	v5s + GhB+αIoU	90.3	10.5	4.9	31
	v5s + GhB+αIoU + SAHI	93.8	10.5	4.9	38

Figure 6. The visual image of the PASCAL VOC dataset test.

Table 5. The detection performance of different detection methods on BDD100 K and Nuscens datasets.

Dataset	Type	AP/%	APs/%	GFLOPs	Parameters/M	Speed/ms
BDD100K	YOLOv5s	62.3	16.4	16	7.1	34
	YOLOX-s	64.2	16.9	26.8	9	35
	PPYOLOE-s	66.5	17.2	17.36	7.93	37
	YOLOv7	67.8	18.1	360	70.4	43
	IVP-YOLOv5	67.1	18.5	10.5	4.9	38
Nuscens	YOLOv5s	68.5	17.8	16	7.1	33
	YOLOX-s	70.7	18.3	26.8	9	35
	PPYOLOE-s	72.4	18.7	17.36	7.93	36
	YOLOv7	74.1	19.2	360	70.4	42
	IVP-YOLOv5	73.6	19.4	10.5	4.9	37

Table 6. The comparison of pedestrian detection results of other detection methods on the BDD100 K dataset.

Type	AP/%	GFLOPs	Parameters/M
SCAM-YOLOv5 (Run-Hua et al., 2022)	63.7	18	7.84
RetinaNet (Haris & Glowacz, 2021)	64.05	359.3	471.7
ICBAM-CenterNet (Li et al., 2022)	66.8	146.6	3.212
IVP-YOLOv5	67.1	10.5	4.9

Figure 7. Comparison chart of detection effect before and after improvement.

Figure 8. Real campus traffic road scenario detection results.

Figure 9. Intelligent vehicle experiment platform and camera location.

Figure 10. Intelligent Vehicle Test Results.

Run-Hua, H., Luo, Q., Zhijian, Y., Yang, W., Guotong, L., & Jianfan, L. (2022). SCAM-YOLOv5: Improved YOLOv5 based on spatial and channel attention module. International conference on computer engineering and networks (pp. 1001–1008), Springer.

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Haris, M., & Glowacz, A. (2021). Road object detection: A comparative study of deep learning-based algorithms. Electronics, 10(16), 1932. https://doi.org/10.3390/electronics10161932

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