Efficient and Accurate Object Detection with Asymmetric Progressive Semi-Decoupled Head and Harmonic Focal Loss

Efficiently and accurately recognizing interesting objects within the image and regressing bounding boxes to enclose them has been a persistent pursuit in object detection. However, existing detectors fail to achieve both aspects simultaneously due to insufficient task interaction and suboptimal classification behavior. To solve the problem, this paper proposes a novel detector with Efficient Asymmetric Progressive Semi-Decoupled Head (EAPSDH) and Harmonic Focal Loss (HFL). Specifically, we generalize the detection head into a progressive asymmetric paradigm that performs hierarchical and dynamically recalibrated interaction between classification and localization, enabling iterative mutual enhancement in an efficient manner beyond the prior designs. Meanwhile HFL is proposed to improve classifier optimization by addressing the imbalance between positive and negative samples. HFL dynamically increases the loss weights of positive samples, amplifying their gradient contributions during classifier training, which significantly reduces classification error. By jointly improving task-specific feature representation and classification optimization, EAPSDH and HFL complement each other to alleviate the inconsistency between classification and localization performance, resulting in an efficient and accurate one-stage detector termed EADet. Experimental results on the MS COCO database demonstrate that EADet effectively mitigates the inconsistency between classification and localization performance. Furthermore, EADet achieves a strong trade-off between accuracy and speed, reaching 47.4 AP at 33.2 FPS on the MS COCO with ResNet-101 under the 2× training schedule, demonstrating its effectiveness compared with recent state-of-the-art detectors. Code will be available at https://github.com/HB-X/EADet.