The detection of all sorts of birds has become increasingly important in the fields of ecological balance and biological protection. To tackle the problems of low accuracy, high omission rate and low detection confidence levels in the application of artificial intelligence and deep learning in bird detection, this paper proposes a bird detection method that leverages the YOLOv5s model and incorporates the asymptotic feature pyramid network module (AFPN). Diverging from the conventional pyramid network module (FPN), AFPN offers a more efficient solution, characterized by reduced computation time and memory consumption. It also minimizes conflicts that may arise during feature training. To further enhance the model’s performance and efficiency, the project introduces an object detection regression loss function along with several distinct loss functions. These functions are employed to train and analyze a standardized dataset, enabling the identification of an optimal solution. Through rigorous model construction, training, and repeated testing, notable improvements have been achieved in the accuracy rate and other relevant indicators, meeting the necessary application standards. This refined method exhibits great potential for fulfilling diverse bird detection requirements.

中文翻译：

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基于渐近特征金字塔的 YOLOv5s 用于鸟类检测

各类鸟类的检测在生态平衡和生物保护领域变得越来越重要。针对人工智能和深度学习应用在鸟类检测中准确率低、遗漏率高和检测置信度低的问题，本文提出一种利用YOLOv5s模型并结合渐近特征金字塔网络模块的鸟类检测方法（ AFPN）。与传统的金字塔网络模块（FPN）不同，AFPN 提供了更有效的解决方案，其特点是减少了计算时间和内存消耗。它还最大限度地减少了特征训练期间可能出现的冲突。为了进一步提高模型的性能和效率，该项目引入了对象检测回归损失函数以及几个不同的损失函数。这些函数用于训练和分析标准化数据集，从而识别最佳解决方案。通过严格的模型构建、训练和反复测试，准确率等相关指标取得了显着提升，满足了必要的应用标准。这种改进的方法在满足不同鸟类检测要求方面表现出巨大的潜力。