A Novel Fish Counting Method Based on Multiscale and Multicolumn Convolution Group Network

Yuxuan Zhang, Junfeng Wu, Hong Yu, Shihao Guo, Yizhi Zhou, Jing Li

Abstract: An accurate grasp of the number of fish in the breeding pond or fixed waters can provide an important basis for bait placement and reasonable fishing, and these data can also provide the necessary data support for accurate breeding. Due to the high density of fish in the real underwater environment, the strong occlusion and the large amount of adhesion, it is difficult to count fish, and the accuracy is low. Considering the above issues, we present a new approach to a fish counting method based on a multiscale multicolumn convolution group network. To enhance the counting accuracy and reduce the complexity of the network, this method uses an asymmetric convolution kernel to change the traditional convolution kernel, which increases our network depth and appreciably reduces the size of the network. In the backbone network, a convolutional group is used to replace a single convolutional layer to enhance the learning capacity of the network. The back of the net introduces the spatial structure of the pyramid and the multicolumn dilated convolution, which preserves the different scaling properties of fish data and improves the capabilities of the fish counting algorithm. To check the performance of the algorithm, this work collects and labels the DLOU3 fish dataset suitable for counting fish and conducts simulation experiments on the DLOU3 fish dataset using our algorithm. The experiments are compared with other popular fish counting algorithms in terms of the mean absolute error (MAE) and mean square error (MSE). The MAE and MSE of the final experimental results of our method are 5.36 and 6.56 and 23.67 and 32.52 in the two test sets, respectively, and the best performance among the five groups of algorithms is obtained.

External IDs:dblp:conf/icycsee/Zhang00GZL22