Rethinking Features-Fused-Pyramid-Neck for Object Detection

Multi-head detectors typically employ a features-fused-pyramid-neck for multi-scale detection and are widely adopted in the industry. However, this approach faces feature misalignment when representations from different hierarchical levels of the feature pyramid are forcibly fused point-to-point. To address this issue, we designed an independent hierarchy pyramid (IHP) architecture to evaluate the effectiveness of the features-unfused-pyramid-neck for multi-head detectors. Subsequently, we introduced soft nearest neighbor interpolation (SNI) with a weight downscaling factor to mitigate the impact of feature fusion at different hierarchies while preserving key textures. Furthermore, we present a features adaptive selection method for down sampling in extended spatial windows (ESD) to retain spatial features and enhance lightweight convolutional techniques (GSConvE). These advancements culminate in our secondary features alignment solution (SA) for real-time detection, achieving state-of-the-art results on Pascal VOC and MS COCO. Code will be released at https://github.com/AlanLi1997/rethinking-fpn. This paper has been accepted by ECCV2024 and published on Springer Nature.

Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles

Object detection is a difficult downstream task in computer vision. For the on-board edge computing platforms, a giant model is difficult to achieve the real-time detection requirement. And, a lightweight model built from a large number of the depth-wise separable convolutional layers cannot achieve the sufficient accuracy. We introduce a new method, GSConv, to lighten the model but maintain the accuracy. The GSConv balances the model's accuracy and speed better. And, we provide a design paradigm, slim-neck, to achieve a higher computational cost-effectiveness of the detectors. In experiments, our method obtains state-of-the-art results (e.g. 70.9% mAP0.5 for the SO-DA10M at a speed of ~100FPS on a Tesla T4) compared with the original networks. Code will be open source.