Small object detection on highways via balance feature fusion and task-specific encoding network

Abstract

Detecting small objects on highways is a novel research topic. Due to the small pixel of objects on highways, traditional detectors have difficulty in capturing discriminative features. Additionally, the imbalance of feature fusion methods and the inconsistency between classification and regression tasks lead to poor detection performance on highways. In this paper, we propose a balance feature fusion and task-specific encoding network to address these issues. Specifically, we design a balance feature pyramid network (FPN) to integrate the importance of each layer of feature maps and construct long-range dependencies among them, thereby making the features more discriminative. In addition, we present task-specific decoupled head, which utilizes task-specific encoding to moderate the imbalance between the classification and regression tasks. As demonstrated by extensive experiments and visualizations, our method obtains outstanding detection performance on small object detection on highways (HSOD) dataset and AI-TOD dataset.

References

[[1]] Wang C Y, Bochkovskiy A, Liao H M. . Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C], 2023 New York IEEE 7464-7475

[[2]] Carion N, Massa F, Synnaeve G, et al.. . End-to-end object detection with transformers[C], 2020 Berlin, Heidelberg Springer 213-229

[[3]] Chan S X, Liu P, Zhang Z. Webox: locating small objects from weak edges[J]. Optoelectronics letters, 2021, 17(6): 349-353,

[[4]] Kim S, Kook H, Sun J, et al.. . Parallel feature pyramid network for object detection[C], 2018 Berlin, Heidelberg Springer 239-256

[[5]] Liu S, Qi L, Qin H, et al.. . Path aggregation network for instance segmentation[C], 2018 New York IEEE 8759-8768

[[6]] Wu Y, Chen Y P, Yuan L, et al.. . Rethinking classification and localization for object detection[C], 2020 New York IEEE 10183-10192

[[7]] GE Z, LIU S T, WANG F, et al. YOLOX: exceeding YOLO series in 2021[EB/OL]. (2021-07-18) [2023-06-24]. https://arxiv.org/abs/2107.08430.

[[8]] BOCHKOVSKIY A, WANG C, LIAO H M. Yolov4: optimal speed and accuracy of object detection[J]. (2020-04-23) [2023-06-24]. https://arxiv.org/abs/2004.10934.

[[9]] Zhang Y F, Ren W Q, Zhang Z, et al.. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146-157,

[[10]] Wang J W, Yang W, Guo H, et al.. . Tiny object detection in aerial images[C], 2021 New York IEEE 3791-3798

[[11]] Lin T, Maire M, Belogie S, et al.. . Microsoft COCO: common objects in context[C], 2014 Berlin, Heidelberg Springer 740-755

[[12]] Girshick R. . Fast R-CNN[C], 2015 New York IEEE 1440-1448

[[13]] Cai Z W, Vasconcelos N. . Cascade R-CNN: delving into high quality object detection[C], 2018 New York IEEE 6154-6162

[[14]] ZHOU X Y, WANG D Q, KR Ä HENB Ü HI P. Objects as points[EB/OL]. (2019-04-25) [2023-06-24]. https://arxiv.org/abs/1904.07850v1.

[[15]] Lu X, Li B Y, Yue Y X, et al.. . Grid R-CNN[C], 2019 New York IEEE 7363-7372

[[16]] Zhang S F, Chi C, Yao Y Q, et al.. . Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C], 2020 New York IEEE 9759-9768

[[17]] Dai X Y, Chen Y P, Xiao B, et al.. . Dynamic head: unifying object detection heads with attentions[C], 2021 New York IEEE 7373-7382 virtual

[[18]] Feng C J, Zhong Y J, Gao Y, et al.. . TOOD: task-aligned one-stage object detection[C], 2021 New York IEEE 3490-3499

[[19]] Zhang H, Li F, Liu S L, et al.. . DINO: DETR with improved denoising anchor boxes for end-to-end object detect ion[C], 2023 New York IEEE

[[20]] Xu C, Wang J W, Yang W, et al.. . Dot distance for tiny object detection in aerial images[C], 2021 New York IEEE 1192-1201 virtual

[[21]] WANG J W, XU C, YANG W, et al. A normalized Gaussian Wasserstein distance for tiny object detection[EB/OL]. (2021-10-26) [2023-06-24]. https://arxiv.org/abs/2110.13389.

[[22]] REDMON J, FARHADI A. Yolov3: an incremental improvement[EB/OL]. (2018-04-08) [2023-06-24]. https://arxiv.org/abs/1804.02767.

[[23]] Kim K, Lee H S. . Probabilistic anchor assignment with iou prediction for object detection[C], 2020 Berlin, Heidelberg Springer 355-371

[[24]] Qiao S Y, Chen L, Yuille A. . DetectoRS: detecting objects with recursive feature pyramid and switchable atrous convolution[C], 2021 New York IEEE 10213-10224 virtual