Dense features are important for detecting minute objects in images. Unfortunately, despite the remarkable efficacy of the CNN models in multi-scale object detection, CNN models often fail to detect smaller objects in images due to the loss of dense features during the pooling process. Atrous convolution addresses this issue by applying sparse kernels. However, sparse kernels often can lose the multi-scale detection efficacy of the CNN model. In this paper, we propose an object detection model using a Switchable (adaptive) Atrous Convolutional Network (SAC-Net) based on the efficientDet model. A fixed atrous rate limits the performance of the CNN models in the convolutional layers. To overcome this limitation, we introduce a switchable mechanism that allows for dynamically adjusting the atrous rate during the forward pass. The proposed SAC-Net encapsulates the benefits of both low-level and high-level features to achieve improved performance on multi-scale object detection tasks, without losing the dense features. Further, we apply a depth-wise switchable atrous rate to the proposed network, to improve the scale-invariant features. Finally, we apply global context on the proposed model. Our extensive experiments on benchmark datasets demonstrate that the proposed SAC-Net outperforms the state-of-the-art models by a significant margin in terms of accuracy.

翻译：密集特征对于检测图像中的微小目标至关重要。然而，尽管CNN模型在多尺度目标检测中表现出卓越效能，但由于池化过程中密集特征的丢失，CNN模型常无法有效检测图像中的较小目标。空洞卷积通过应用稀疏卷积核来解决此问题，但稀疏卷积核往往会导致CNN模型的多尺度检测能力下降。本文基于efficientDet模型，提出了一种采用可切换（自适应）空洞卷积网络（SAC-Net）的目标检测模型。固定空洞率限制了CNN模型在卷积层中的性能表现。为克服此限制，我们引入了一种可切换机制，可在前向传播过程中动态调整空洞率。所提出的SAC-Net融合了低层与高层特征的优势，在保持密集特征不丢失的前提下，提升了多尺度目标检测任务的性能。此外，我们在网络中应用深度可切换空洞率以增强尺度不变特征。最后，我们在模型中引入全局上下文信息。通过在基准数据集上的大量实验表明，所提出的SAC-Net在检测精度方面显著优于现有最先进模型。