Zheng Zhiqiang, Wang Mengbo, Zhao Xiaoyu, Weng Zhi
College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China.
State Key Laboratory of Reproductive Regulation & Breeding of Grassland Livestock, Hohhot 010030, China.
Animals (Basel). 2024 Dec 17;14(24):3635. doi: 10.3390/ani14243635.
This study proposes an image enhancement detection technique based on Adltformer (Adaptive dynamic learning transformer) team-training with Detr (Detection transformer) to improve model accuracy in suboptimal conditions, addressing the challenge of detecting cattle in real pastures under complex lighting conditions-including backlighting, non-uniform lighting, and low light. This often results in the loss of image details and structural information, color distortion, and noise artifacts, thereby compromising the visual quality of captured images and reducing model accuracy. To train the Adltformer enhancement model, the day-to-night image synthesis (DTN-Synthesis) algorithm generates low-light image pairs that are precisely aligned with normal light images and include controlled noise levels. The Adltformer and Detr team-training (AT-Detr) method is employed to preprocess the low-light cattle dataset for image enhancement, ensuring that the enhanced images are more compatible with the requirements of machine vision systems. The experimental results demonstrate that the AT-Detr algorithm achieves superior detection accuracy, with comparable runtime and model complexity, reaching 97.5% accuracy under challenging illumination conditions, outperforming both Detr alone and sequential image enhancement followed by Detr. This approach provides both theoretical justification and practical applicability for detecting cattle under challenging conditions in real-world farming environments.
本研究提出了一种基于Adltformer（自适应动态学习变压器）与Detr（检测变压器）团队训练的图像增强检测技术，以提高在次优条件下的模型准确性，解决在复杂光照条件下（包括逆光、光照不均匀和低光）在实际牧场中检测牛的挑战。这通常会导致图像细节和结构信息丢失、颜色失真以及噪声伪影，从而损害捕获图像的视觉质量并降低模型准确性。为了训练Adltformer增强模型，昼夜图像合成（DTN-Synthesis）算法生成与正常光照图像精确对齐且包含可控噪声水平的低光图像对。采用Adltformer和Detr团队训练（AT-Detr）方法对低光牛数据集进行预处理以进行图像增强，确保增强后的图像更符合机器视觉系统的要求。实验结果表明，AT-Detr算法实现了卓越的检测精度，运行时间和模型复杂度相当，在具有挑战性的光照条件下准确率达到97.5%，优于单独的Detr以及先进行顺序图像增强再使用Detr的方法。这种方法为在现实农业环境中的具有挑战性条件下检测牛提供了理论依据和实际适用性。
