# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
###### Abstract  
To enhance the accuracy of lightweight CNN classification models in analyzing fish feeding behavior, this paper addresses the image quality issues caused by external environmental factors and lighting conditions, such as low contrast and uneven illumination, by proposing a Multi-step Image Pre-enhancement Strategy (MIPS). This strategy includes three critical steps: initially, images undergo a preliminary processing using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, effectively reducing the impact of water surface reflections and enhancing the visual effect of the images; secondly, the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique is applied to further improve image contrast, especially in areas of low contrast, by adjusting the local contrast levels to enhance the clarity of the image details; finally, Unsharp Masking (UM) technology is employed to sharpen the images, emphasizing their edges to increase the clarity of the image details, thereby significantly improving the overall image quality. Experimental results on a custom dataset have confirmed that this pre-enhancement strategy significantly boosts the accuracy of various CNN-based classification models, particularly for lightweight CNN models, and drastically reduces the time required for model training compared to the use of advanced ResNet models. This research provides an effective technical route for improving the accuracy and efficiency of an image-based analysis of fish feeding behavior in complex environments. multi-step image pre-enhancement strategy; fish feeding behavior analysis; multi-scale retinex with color restoration; unsharp masking +
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and ensuring the healthy growth of fish stocks. It is anticipated that these systems will gradually replace experience-based manual feeding decisions in the future. The spatial characteristics [8; 9] of fish stocks undergo significant changes during the feeding process, making fish feeding behavior an important indicator of fish appetite. Among various behavior analysis methods, computer vision [10] is an efficient, non-contact detection [11; 12] technology which has been widely applied in the analysis of fish behavior. Traditional methods primarily quantify the intensity of fish feeding activities by extracting color and texture features from feeding images. Numerous studies utilize image segmentation [13] or object detection techniques to extract fish coordinates and calculate their swimming speeds [14] and aggregation levels for the analysis of fish feeding behavior per unit time. These methods can rapidly capture the behavioral characteristics of fish. However, considering the complexity of lighting conditions in industrialized farming and the randomness of fish behavior changes [15], conventional methods based on manually designed features struggle to accurately extract the characteristics of fish feeding behavior. Since the victory of AlexNet [16] in the ImageNet competition in 2012, convolutional neural networks (CNNs) have garnered widespread attention. Unlike traditional feature extraction methods, CNNs do not require features to be manually designed. In complex tasks, CNNs demonstrate higher robustness and accuracy compared to conventional methods. In recent years, an increasing number of deep learning models have been applied in aquaculture. [PERSON] et al. [17] proposed a method for analyzing fish feeding activities using LeNet-5, which combines a CNN and computer vision, categorizing fish feeding intensity into four states with a classification accuracy of 90%, effectively assessing fish feeding behavior. [PERSON] et al. [18] employed deep learning technologies, including an improved R(2 + 1)D convolutional neural network (CNN), to identify the size of water ripples produced during fish feeding to decide whether to continue feeding.