Zhang L et al. (2022), Sea Cucumber Detection Algorithm Based on Deep ...

ECB-ART-50962

Sensors (Basel) 2022 Jul 30;2215:. doi: 10.3390/s22155717.

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Sea Cucumber Detection Algorithm Based on Deep Learning.

Zhang L , Xing B , Wang W , Xu J .

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The traditional single-shot multiBox detector (SSD) for the recognition process in sea cucumbers has problems, such as an insufficient expression of features, heavy computation, and difficulty in application to embedded platforms. To solve these problems, we proposed an improved algorithm for sea cucumber detection based on the traditional SSD algorithm. MobileNetv1 is selected as the backbone of the SSD algorithm. We increase the feature receptive field by receptive field block (RFB) to increase feature details and location information of small targets. Combined with the attention mechanism, features at different depths are strengthened and irrelevant features are suppressed. The experimental results show that the improved algorithm has better performance than the traditional SSD algorithm. The average precision of the improved algorithm is increased by 5.1%. The improved algorithm is also more robust. Compared with YOLOv4 and the Faster R-CNN algorithm, the performance of this algorithm on the P-R curve is better, indicating that the performance of this algorithm is better. Thus, the improved algorithm can stably detect sea cucumbers in real time and provide reliable feedback information.

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References [+] :

Feng, SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks. 2021, Pubmed

	Figure 1. Network-structure diagram of traditional SSD algorithm.
	Figure 2. Output results from different levels of SSD.
	Figure 3. Underwater robots taking photographs of sea cucumbers.
	Figure 4. Standard convolution.
	Figure 5. Depthwise convolution.
	Figure 6. Pointwise convolution.
	Figure 7. Comparison of traditional and expansive convolution.
	Figure 8. RFB module processing flowchart.
	Figure 9. Improved SSD network structure.
	Figure 10. The loss curve.
	Figure 11. Comparison of P-R curves before and after model improvement.
	Figure 12. Comparison of sea cucumber identification before and after model improvement.
	Figure 13. Four models comparison of P-R curves.