Recognition and localization of freshwater fish heads and tails based on lightweight neural networks

Figures & data

Figure 1. Schematic diagram of the acquisition device. 1- Camera, 2- Freshwater, 3- Conveyor Belt.

Figure 2. Four fish images. (a) silver carp. (b) Carp. (c) Perch. (d) Bighead carp.

Table 1. Category and quantity of Freshwater fish datasets.

Type of Freshwater fish	Training set	Test set	Validation set
Silver carp	306	102	102
Carp	240	80	80
Perch	440	148	148
Bighead carp	214	70	70

Figure 3. Structure diagram of YOLOV3.

Figure 4. Flow chart of fish head and fish tail detection.

Table 2. Parameters related to the experimental environment.

Category name	Parameter
Operating system	Windows 11
CPU	12th Gen Intel(R) Core(TM) i7-12700H
GPU	NVIDIA GeForce RTX 3050
Cuda with Cudnn	11.6/8.6.4
Python	3.7
Tensorflow,Keras	1.13.2/2.2.5

Figure 5. YOLOV3-Darknet53 detection effect.

Table 3. Comparison table of detection accuracy and time of the four models.

Model	Average testing time(ms)	Average detection accuracy(%)
SSD-VGG16	114.2	93.27
SSD-MobileNetv3	38.19	94.12
YOLOv3-Darkne53	57.20	96.29
YOLOV3-MobileNetV3	28.2	98.36

Figure 6. The loss curve of the freshwater fish dataset. (1) SSD-VGG16; (2) SSD-MobileNetV3.

Figure 7. The actual test result for the freshwater fish dataset. (a) The test results of YOLOv3-MobileNetv3. (b) The test results of SSD-MobileNetv3. (c) The test results of SSD-VGG16.

Figure 8. The loss curve of the freshwater fish dataset. (1) YOLOV3-Darknet-53; (2) YOLOV3-MobileNetV3.

Figure 9. The comparison of four deep learning-based target detection models. (a) Time comparison. (b) Accuracy comparison.