Recently, many excellent algorithms have made great progress in object detection, but there are also problems in these algorithms’ performance on targets of different sizes, and in particular in small object detection. Aiming at the problem of insufficient feature representation by the feature extractor, in this paper we propose a lightweight algorithm to improve feature extraction. The algorithm includes three modules. First, considering that the shallow features in feature extraction contain much background noise, in this paper we design a multi-level feedback propagation model based on a Gaussian high-pass filter. The shallow layers are enhanced using the filter and then back-propagated to add the upper shallow layer features and obtain new shallow layer features. This process is performed on the newly generated shallow layer for n iterations, which is beneficial for enhancing targets in the foreground area and suppressing background noise. Second, we form a stacked dilated convolution module with different dilation rates to cover the entire deep feature layer densely, which enlarges the receptive field and enriches the contextual information. Finally, we build a multi-scale fusion module to fuse the above-mentioned enhanced shallow and deep features to obtain output features with powerful representational ability for detection tasks. In addition, the model is easily embedded into existing approaches to enhance their performance. We build the model on the VGG-16 and ResNet-50 backbones and successfully applied it on Darknet-19 and Darknet-53 to verify its effectiveness and stability. The experiments on the COCO dataset prove that the proposed algorithm outperforms the state-of-art methods, with a mean average precision improvement reaching 2% on average. The effect is remarkable on small targets and complex backgrounds. Furthermore, it does not affect the detection speed significantly, so real time detection requirements can still be met.

近年来，许多优秀的算法在物体检测方面取得了很大进展，但这些算法在对不同尺寸目标的性能上也存在问题，尤其是在小物体检测方面。针对特征提取器的特征表示不足的问题，本文提出了一种轻量级算法来改进特征提取。该算法包括三个模块。首先，考虑到特征提取中的浅层特征包含较多的背景噪声，本文设计了一种基于高斯高通滤波器的多级反馈传播模型。使用滤波器增强浅层，然后反向传播以添加上层浅层特征并获得新的浅层特征。这个过程是在新生成的浅层上执行的n迭代，有利于增强前景区域的目标和抑制背景噪声。其次，我们形成了一个具有不同扩张率的堆叠扩张卷积模块来密集地覆盖整个深层特征层，从而扩大了感受野并丰富了上下文信息。最后，我们构建了一个多尺度融合模块来融合上述增强的浅层和深层特征，以获得对检测任务具有强大表示能力的输出特征。此外，该模型很容易嵌入到现有方法中以提高其性能。我们在 VGG-16 和 ResNet-50 主干上构建模型，并成功将其应用于 Darknet-19 和 Darknet-53 以验证其有效性和稳定性。在 COCO 数据集上的实验证明，所提出的算法优于最先进的方法，平均精度提高平均达到 2%。在小目标和复杂背景上效果显着。此外，它不会显着影响检测速度，因此仍然可以满足实时检测要求。