Detection Transformer (DETR) and its variants have shown great potential for accurate object detection in recent years. The mechanism of object query enables DETR family to directly obtain a fixed number of object predictions and streamlines the detection pipeline. Meanwhile, recent studies also reveal that with proper architecture design, convolution networks (ConvNets) also achieve competitive performance with transformers, \eg, ConvNeXt. To this end, in this paper we explore whether we could build a query-based end-to-end object detection framework with ConvNets instead of sophisticated transformer architecture. The proposed framework, \ie, Detection ConvNet (DECO), is composed of a backbone and convolutional encoder-decoder architecture. We carefully design the DECO encoder and propose a novel mechanism for our DECO decoder to perform interaction between object queries and image features via convolutional layers. We compare the proposed DECO against prior detectors on the challenging COCO benchmark. Despite its simplicity, our DECO achieves competitive performance in terms of detection accuracy and running speed. Specifically, with the ResNet-50 and ConvNeXt-Tiny backbone, DECO obtains $38.6\%$ and $40.8\%$ AP on COCO \textit{val} set with $35$ and $28$ FPS respectively and outperforms the DETR model. Incorporated with advanced multi-scale feature module, our DECO+ achieves $47.8\%$ AP with $34$ FPS. We hope the proposed DECO brings another perspective for designing object detection framework.

翻译：检测Transformer（DETR）及其变体近年来在精确目标检测方面展现出巨大潜力。对象查询机制使DETR系列能够直接获得固定数量的目标预测，并简化了检测流程。与此同时，最新研究也表明，通过合理的架构设计，卷积网络（ConvNets）同样能取得与Transformer相媲美的性能，例如ConvNeXt。为此，本文探索是否能够构建一个基于查询的端到端目标检测框架，该框架采用卷积神经网络而非复杂的Transformer架构。所提出的检测卷积网络（DECO）包含骨干网络和卷积编码器-解码器架构。我们精心设计了DECO编码器，并提出一种新颖机制，使DECO解码器能够通过卷积层实现对象查询与图像特征之间的交互。在极具挑战性的COCO基准上，我们将DECO与先前检测器进行对比。尽管设计简洁，DECO在检测精度和运行速度方面均表现出竞争力。具体而言，基于ResNet-50和ConvNeXt-Tiny骨干网络，DECO在COCO验证集上分别以35FPS和28FPS达到38.6%和40.8%的平均精度，性能优于DETR模型。结合先进的多尺度特征模块后，DECO+在34FPS下实现了47.8%的平均精度。我们希望所提出的DECO能为目标检测框架设计提供新视角。