# A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet
###### Abstract
They developed a computer vision-based intelligent fish feeding system that not only recognizes the dynamics of water ripples but also integrates data from water quality sensors, achieving an accuracy of 93.2%. [PERSON] [19] and colleagues utilized a Spatio-Temporal Attention Network (STAN) to analyze the feeding behavior of Pompano fish stocks. By innovatively combining spatial images [20] with optical flow images [21; 22; 23] for video analysis, the study applied the STAN model to extract intuitive and perceptual features to determine the feeding or non-feeding states of Pompano fish stocks. This method incorporated a hierarchical convolutional network (HCN) to extract multi-scale spatial features. Experimental validation showed that the STAN model achieved a test accuracy of 97.97%. ResNet (Residual Network [24]), another widely used deep learning model, has also shown its effectiveness in fish feeding behavior classification tasks. By utilizing residual connections to prevent the vanishing gradient problem, ResNet models can achieve commendable accuracy. However, they typically possess a large number of parameters, necessitating more computational resources and training time. This makes them less suitable for resource-constrained small devices, such as smartphones. However, the image quality of fish feeding behavior is affected by factors such as the fish pond environment and lighting conditions, leading to issues like low contrast and uneven illumination, which result in less prominent foreground features in the images. Additionally, due to the relatively small area of individual fish in images, CNN models can easily be disrupted by background features during the feature extraction process. This makes it challenging for traditional CNN models to focus on fish behavior [25; 26; 27; 28; 29], reducing the accuracy of model classification. To tackle this issue, our research introduces a multi-step image pre-enhancement strategy that sequentially employs the MSRCR (Multi-Scale Retinex with Color Restoration), Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc), and Unsharp Masking (UM) techniques for image pre-enhancement. This approach is designed to eliminate problems related to water reflections, low contrast, and unclear image detail features. Following this, the lightweight [30; 31; 32] EfficientNet model is utilized for classifying fish feeding behaviors. The test results demonstrate that the proposed MIPS (Multi-Step Image Preprocessing Strategy) module significantly enhances the accuracy of various CNNmodels in classification tasks. Furthermore, compared to advanced ResNet [33] models, it markedly reduces the training time of the model. The key contributions can be summarized as the following three points:  
(1) This study proposes a multi-step image pre-enhancement strategy by integrating existing techniques such as color space conversion, Multi-Scale Retinex with Color Restoration (MSRCR), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Unsharp Masking (USM). This integration significantly improves the quality of underwater fish feeding behavior images. (2) The main improvements include two parts: First, an innovative multi-parameter-driven CLAHE method is devised, which involves the detailed processing of only the luminance channel in the LAB color space. By carefully optimizing the clipLimit and tileGridSize parameters, this method balances local detail enhancement and overall visual coherence. Second, an adaptive image enhancement adjustment mechanism is proposed, based on comprehensive evaluation metrics, including Learned Perceptual Image Patch Similarity (LPIPS), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). This mechanism dynamically adjusts the degree of image enhancement, ensuring that the results meet both subjective visual perception and objective quality standards. (3) The present study presents a comprehensive system architecture for classifying the feeding intensity of fish.