Object Detection via Multimodal Adaptive Feature Fusion

Xiaoqiang Gao; Kan Chang; Mingyang Ling; Mengyu Yin

doi:10.3788/LOP230856

[1] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[2] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[3] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[4] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934

[5] Zhang Y, Zhu G Y, Shi T J et al. Small object detection in remote sensing images based on feature fusion and attention[J]. Acta Optica Sinica, 42, 2415001(2022).

[6] Xu Z J, Bai X. Small ship target detection method for remote sensing images based on dual feature enhancement[J]. Acta Optica Sinica, 42, 1828002(2022).

[7] Ju C R, Qin X Y, Yuan G L et al. Fast small object detection method with scale-sensitivity loss and feature fusion[J]. Acta Electronica Sinica, 50, 2119-2126(2022).

[8] Li X, He M, Luo H B. Occluded pedestrian detection algorithm based on improved YOLOv3[J]. Acta Optica Sinica, 42, 1415003(2022).

[9] Wang Y W, Guo Y, Shao X Y. Remote sensing image target detection based on improved cascade algorithm[J]. Acta Optica Sinica, 42, 2428004(2022).

[10] Xue J D, Zhu J J, Zhang J et al. Object detection in optical remote sensing images based on FFC-SSD model[J]. Acta Optica Sinica, 42, 1210002(2022).

[11] Lin J P, Haberstroh F, Karsch S et al. Applications of object detection networks in high-power laser systems and experiments[J]. High Power Laser Science and Engineering, 11, e7(2023).

[12] Osornio-Rios R A, Antonino-Daviu J A, de Jesus Romero-Troncoso R. Recent industrial applications of infrared thermography: a review[J]. IEEE Transactions on Industrial Informatics, 15, 615-625(2019).

[13] Dai X R, Yuan X, Wei X Y. TIRNet: object detection in thermal infrared images for autonomous driving[J]. Applied Intelligence, 51, 1244-1261(2021).

[14] He Z F, Chen G C, Chen J S et al. Multi-scale feature fusion lightweight real-time infrared pedestrian detection at night[J]. Chinese Journal of Lasers, 49, 1717002(2022).

[15] Song Z Z, Yang J W, Zhang D F et al. Low-altitude Sea surface infrared object detection based on unsupervised domain adaptation[J]. Acta Optica Sinica, 42, 0415001(2022).

[16] Xu D, Ouyang W, Ricci E et al. Learning cross-modal deep representations for robust pedestrian detection[C], 5363-5371(2017).

[17] Li C Y, Song D, Tong R F et al. Multispectral pedestrian detection via simultaneous detection and segmentation[EB/OL]. https://arxiv.org/abs/1808.04818

[18] Devaguptapu C, Akolekar N, Sharma M M et al. Borrow from anywhere: pseudo multi-modal object detection in thermal imagery[C], 1029-1038(2020).

[19] Zhang L, Zhu X Y, Chen X Y et al. Weakly aligned cross-modal learning for multispectral pedestrian detection[C], 5126-5136(2020).

[20] Liu T, Gao S J, Nie W Z. Multitarget detection algorithm based on multimodal information fusion[J]. Laser & Optoelectronics Progress, 59, 0815002(2022).

[21] Wagner J, Fischer V, Herman M et al. Multispectral pedestrian detection using deep fusion convolutional neural networks[C], 509-514(2016).

[22] Liu J J, Zhang S T, Wang S et al. Multispectral deep neural networks for pedestrian detection[EB/OL]. https://arxiv.org/abs/1611.02644

[23] Konig D, Adam M, Jarvers C et al. Fully convolutional region proposal networks for multispectral person detection[C], 243-250(2017).

[24] Kieu M, Bagdanov A D, Bertini M et al. Task-conditioned domain adaptation for pedestrian detection in thermal imagery[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12367, 546-562(2020).

[25] Zhang H, Fromont E, Lefevre S et al. Multispectral fusion for object detection with cyclic fuse-and-refine blocks[C], 276-280(2020).

[26] Zhou K L, Chen L S, Cao X. Improving multispectral pedestrian detection by addressing modality imbalance problems[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12363, 787-803(2020).

[27] Zhang H, Fromont E, Lefevre S et al. Guided attentive feature fusion for multispectral pedestrian detection[C], 72-80(2021).

[28] Fang Q Y, Han D P, Wang Z K. Cross-modality fusion transformer for multispectral object detection[EB/OL]. https://arxiv.org/abs/2111.00273

[29] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 5998-6008(2017).

[30] Li X, Wang W H, Hu X L et al. Selective kernel networks[C], 510-519(2020).

[31] Wang Q, Wu B, Zhu P et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C], 11534-11542(2020).

[32] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[33] Lin T Y, Maire M, Belongie S et al. Microsoft coco: common objects in context[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 8693, 740-755(2014).

[35] Jia X Y, Zhu C, Li M Z et al. LLVIP: a visible-infrared paired dataset for low-light vision[C], 3489-3497(2021).

[36] Chen Y T, Shi J, Ye Z et al. Multimodal object detection via probabilistic ensembling[M]. Avidan S, Brostow G, Cissé M, et al. Computer vision-ECCV 2022. Lecture notes in computer science, 13669, 139-158(2022).

[37] Yan C Q, Zhang H, Li X L et al. Cross-modality complementary information fusion for multispectral pedestrian detection[J]. Neural Computing and Applications, 1-26(2023).