MS-YOLOv5: a lightweight algorithm for strawberry ripeness detection based on deep learning

Figures & data

Figure 1. Improved diagram of the framework network structure for MS YOLOv5.

Figure 2. Depth hybrid deformable convolution.

Figure 3. Double cooperative attention mechanism.

Figure 4. PA attention mechanism module.

Figure 5. Fast-weighted feature pyramid network.

Table 1. Division of the dataset in the experiments.

Dataset category	Training set	Validation set	Test set
Ripe and Unripe	6676	952	1908

Figure 6. Data enhancement of original images.

Figure 7. Display of ripe and unripe strawberries in the dataset.

Table 2. Ablation experiments of MS-YOLOv5.

A	B	C	F1-score%	Recall%	mAP%	FPS	Parameters	GFLOPS	Model Size (M)
–	–	–	86	86.0	87.2	75	9014256	18.0	13.72
√	–	–	86	88.5	90.6	82	7025023	12.0	11.0
√	√	–	87	89.9	93.3	77	7502506	16.0	15.2
√	√	√	91	93.2	95.6	76	7015519	12.9	7.44

Table 3. Comparison table of YOLOv5 with different attention mechanisms.

Methods	mAP%	Model Size (M)	FPS
YOLOv5 (baseline)	87.2	13.72	75
YOLOv5 + SE	89.1	16.0	73
YOLOv5 + CA	91.3	14.2	76
YOLOv5 + CBAM	90.6	17.1	72
YOLOv5+ BC-attention (ours)	92.6	15.5	74

Figure 8. Visualization of the effects of applying different attentional mechanisms to the features of YOLOv5.

Figure 9. Confusion matrix comparison between MS-YOLOv5 and YOLOv5.

Figure 10. Precision_Recall comparison between MS-YOLOv5 and YOLOv5.

Table 4. Table comparing MS-YOLOv5 with state-of-the-art methods.

Methods	mAP%	Recall%	Unripe Strawberry%	Ripe Strawberry %	Model Size (M)	FPS
YOLOv3	79.9	75.6	71.4	88.4	120.7	55.0
YOLOv5	87.2	86.0	89.3	85.0	13.72	75.0
Faster RCNN	88.5	92.3	89.9	87.1	458.2	0.12
YOLOv4	89.4	85.3	85.3	93.5	250.2	69.0
Cascade RCNN	92.1	93.4	91.5	92.7	671.6	0.48
YOLOv6	92.8	90.2	89.2	96.5	39.3	61.0
YOLOv7	93.7	91.6	92.1	95.2	71.4	72.0
Libra RCNN	93.7	91.0	92.5	94.9	366.0	–
YOLOv8	95.2	92.2	93.6	96.7	18.1	75.0
MS-YOLOv5	95.6	93.2	94.4	96.9	7.44	76.0

Figure 11. Detection effect under different conditions between YOLOv5 and MS-YOLOv5.