DE-CrossDet: Divisible and Extensible Crossline Representation for Object Detection

Object detection aims to localize and classify objects. Suitable object representation plays an important role in accurate detection. Because a complete crossline inevitably passes through the noise of backgrounds or other objects, object features directly extracted by the whole crossline are often confused. In this paper, we present a new feature extraction method, DE-Crossline, which can enhance the original crossline representation to capture more accurate object information. Specifically, we divide the crossline into several segments, each of which extracts the maximum activation key point respectively to reduce the impact of noise mentioned above. Furthermore, considering various shapes and sizes of objects, we design a Deformable Width Extension Module to learn a suitable width of each crossline, so as to capture richer object information. Extensive experiments prove the effectiveness of our proposed method. The total performance of our proposed detector can reach 49.0% AP, using ResNet-101 as backbone on the MS-COCO dataset.