In order to better meet the accuracy and timeliness requirements of Chinese food dish recognition， a new type of dish recognition network was designed. The original YOLOv5 model was pruned by combining Supermask method and structured channel pruning method， and lightweighted finally by Int8 quantization technology. This ensured that the proposed model could balance accuracy and speed in dish recognition， achieving a good trade-off while improving the model portability. Experimental results show that the proposed model achieves a mean Average Precision （mAP） of 99.00% and an average recognition speed of 59.54 ms /frame at an Intersection over Union （IoU） of 0.5， which is 20 ms/frame faster than that of the original YOLOv5 model while maintaining the same level of accuracy. In addition， the new dish recognition network was ported to the Renesas RZ/G2L board by Qt. Based on this， an intelligent service system was constructed to realize the whole process of ordering， generating orders， and automatic meal distribution. A theoretical and practical foundation was provided for the future construction and application of truly intelligent service systems in restaurants.

Aquaculture counting is an important part of the aquaculture process， and the counting results provide an important basis for feeding， breeding density adjustment， and economic efficiency estimation of aquatic animals. In response to the traditional manual counting methods， which are time-consuming， labor-intensive， and prone to large errors， a large number of methods and applications based on machine vision have been proposed， thereby greatly promoting the development of non-destructive counting of aquatic products. In order to deeply understand the research on aquaculture counting based on machine vision， the relevant domestic and international literature in the past 30 years was collated and analyzed. Firstly， a review of aquaculture counting was presented in the perspective of data acquisition， and the methods for acquiring the data required for machine vision were summed up. Secondly， the aquaculture counting methods were analyzed and summarized in terms of traditional machine vision and deep learning. Thirdly， the practical applications of counting methods in different farming environments were compared and analyzed. Finally， the difficulties in the development of aquaculture counting research were summarized in terms of data， methods， and applications， and corresponding views were presented for the future trends of aquaculture counting research and equipment applications.