# FE-P Net: An Image-Enhanced Parallel Density Estimation Network for Meat Duck Counting
###### Abstract  
Traditional object detection methods for meat duck counting suffer from high manual costs, low image quality, and varying object sizes. To address these issues, this paper proposes FE-P Net, an image enhancement-based parallel density estimation network that integrates CNNs with Transformer models. FE-P Net employs a Laplacian pyramid to extract multi-scale features, effectively reducing the impact of low-resolution images on detection accuracy. Its parallel architecture combines convolutional operations with attention mechanisms, enabling the model to capture both global semantics and local details, thus enhancing its adaptability across diverse density scenarios. The Reconstructed Convolution Module is a crucial component that helps distinguish targets from backgrounds, significantly improving feature extraction accuracy. Validated on a meat duck counting dataset in breeding environments, FE-P Net achieved 96.46% accuracy in large-scale settings, demonstrating state-of-the-art performance. The model shows robustness across various densities, providing valuable insights for poultry counting methods in agricultural contexts. Keywords:Transformer; meat duck counting method; large-scale breeding; image enhancement; multi-scale features +
Footnote †: journal: Medical  
densities and streamline farming processes. Consequently, while lowering labor costs, they contribute to an overall enhancement in productivity and economic returns [4]. [PERSON] et al. [5] introduced the R-CNN algorithm, marking a significant advancement in object detection. However, R-CNN was constrained by its requirement for fixed-size candidate regions, leading to slower detection speeds and substantial storage needs for region proposals. To address these limitations, Fast R-CNN [6] and Faster R-CNN [7] were subsequently developed. Fast R-CNN incorporated Spatial Pyramid Pooling (SPP) to accelerate processing, while Faster R-CNN introduced Region Proposal Networks (RPNs) to significantly boost both speed and detection accuracy. In large-scale breeding, high poultry density often leads to occlusion and crowding. Traditional machine learning methods struggle to perform effectively under such conditions. [PERSON] et al. [8] pioneered the use of deep learning for crowd counting with a multi-column convolutional neural network, marking the first application of this technique to counting tasks. Although object detection-based counting methods have demonstrated effectiveness, they require significant manual effort for bounding box annotation, which is both costly and time-consuming. In densely occluded real-world settings, these methods often produce inaccurate counts. To overcome these challenges, [PERSON] et al. [9] enhanced the point-supervised localization and counting method proposed by [PERSON] et al. [10], aiming to enable real-time processing of camera-captured images. By using DenseNet, introduced by [PERSON] et al. [11], as the backbone network and employing point annotation instead of bounding boxes, their approach significantly reduces annotation time and boosts data processing efficiency compared to traditional methods. Since then, research has advanced these methods, particularly in breeding scenarios. [PERSON] et al. [12] developed a 13-layer convolutional network combining Count CNN [13] and ResNeXt [14] for pig counting, achieving low error rates on sparse datasets. [PERSON] et al. [15] addressed fish counting on dense datasets using dilated convolutions to increase the receptive field without reducing resolution and channel attention modules to enhance feature selection, achieving a mean absolute error (MAE) of 7.07 and a mean squared error (MSE) of 19.16. [PERSON] et al. [16] used a multi-column neural network with \(1\times 1\) convolutions replacing fully connected layers, resulting in an MAE of 3.33 and MSE of 4.58 on a dataset of 3200 seedling images. [PERSON] et al.