Pest counting, which predicts the number of pests in the early stage, is very important because it enables rapid pest control, reduces damage to crops, and improves productivity. In recent years, light traps have been increasingly used to lure and photograph pests for pest counting. However, pest images have a wide range of variability in pest appearance owing to severe occlusion, wide pose variation, and even scale variation. This makes pest counting more challenging. To address these issues, this study proposes a new pest counting model referred to as multiscale and deformable attention CenterNet (Mada-CenterNet) for internal low-resolution (LR) and high-resolution (HR) joint feature learning. Compared with the conventional CenterNet, the proposed Mada-CenterNet adopts a multiscale heatmap generation approach in a two-step fashion to predict LR and HR heatmaps adaptively learned to scale variations, that is, changes in the number of pests. In addition, to overcome the pose and occlusion problems, a new between-hourglass skip connection based on deformable and multiscale attention is designed to ensure internal LR and HR joint feature learning and incorporate geometric deformation, thereby resulting in an improved pest counting accuracy. Through experiments, the proposed Mada-CenterNet is verified to generate the HR heatmap more accurately and improve pest counting accuracy owing to multiscale heatmap generation, joint internal feature learning, and deformable and multiscale attention. In addition, the proposed model is confirmed to be effective in overcoming severe occlusions and variations in pose and scale. The experimental results show that the proposed model outperforms state-of-the-art crowd counting and object detection models.

翻译：害虫计数是非常重要的，因为它可以预测早期害虫数量，实现快速害虫控制，减少对作物的损害，提高生产力。近年来，光陷阱逐渐被用于引诱和拍摄害虫进行害虫计数。然而，由于严重的遮挡、广泛的姿态变化甚至尺度变化，害虫图像的害虫外观有很大的差异，这使得害虫计数更具挑战性。为了解决这些问题，本研究提出了一种新的害虫计数模型，称为集成内部低分辨率(LR)和高分辨率(HR)联合特征学习的多尺度和可变形的注意力中心网络。与传统的中心网络相比，所提出的Mada-CenterNet采用双步骤的多尺度热图生成方法来自适应地学习预测LR和HR热图以适应尺度变化，即害虫数量的变化。此外，为了克服姿态和遮挡的问题，设计了一种新的基于可变形和多尺度注意力的变步长连接方式，以确保内部LR和HR联合特征学习，并集成几何变形，从而导致准确性的提高。通过实验，验证了所提出的Mada-CenterNet在多尺度热图生成、联合内部特征学习和可变形和多尺度注意力方面具有更高的HR热图生成精度，并提高害虫计数精度。此外，所提出的模型被证明可以有效地克服严重的遮挡和姿态和尺度的变化。实验结果表明，与最先进的人群计数和对象检测模型相比，所提出的模型表现更好。