Journal of Infrared and Millimeter Waves, Volume. 41, Issue 6, 1092(2022)
GPNet:Lightweight infrared image target detection algorithm
[1] Han J, Yu Y, Liang K et al. Infraredsmall-target detection under complex background based on subblock-level ratio-difference joint local contrast measure[J]. Optical Engineering, 57, 103105(2018).
[2] LI Tong-shun, XI Yong, YIN Jian-Fei. Analysis of the development of key technologies for air-to-air infrared gui-dance[J]. Shanghai Aerospace.
[3] Fang L, Wang X, Wan Y. Adaptable active contour model with applicationsto infrared ship target segmentation[J]. Journal of Electronic Imaging, 25, 041010(2016).
[4] Zhang L, Wu B, Nevatia R. Pedestrian detection in infrared images based on local shape features[C], 1-8(2007).
[5] Ge J, Luo Y, Tei G. Real-time pedestrian detection and tracking at nighttime for driver-assistance systems[J]. IEEE Transactions on Intelligent Transportation Systems, 10, 283-298(2009).
[6] SU Xiao-Qian, SUN Shao-Yuan, GE Man et al. Pedestrian detection and tracking of vehicle infrared images[J]. Laser & Infrared.
[7] ZHU Han-Lu, ZHANG Xu-Zhong, CHEN Xin et al. Dim small targets detection based on horizontal-vertical multi-scale grayscale difference weighted bilateral filtering[J]. J. Infrared Millim. Waves.
[8] CAI Ru-Hua, YANG Biao, WU Sun-Yong et al. Weak Targets Box Particle Labeled Multi-bernoulli Multi-target Detection and Tracking Algorithm[J]. J. Infrared Millim. Waves.
[9] Choi Y, Kim N, Hwang S et al. KAIST multi-spectral day/night data set for autonomous and assisted driving[J]. IEEE Transactions on Intelligent Transportation Systems, 19, 934-948(2018).
[11] Socarrás Y, Ramos S, Vázquez D et al. Adapting pedestrian detection from synthetic to far infrared images[C], 3(2013).
[12] Ghose D, Desai S M, Bhattacharya S et al. Pedestrian detection in thermal images using saliency maps[C], 1-10(2019).
[13] Devaguptapu C, Akolekar N, Sharma M et al. Borrow from anywhere: Pseudo multi-modal object detection inthermal imagery[C], 1029-1038(2019).
[14] Dai X, Yuan X, Wei X. TIRNet: Object detection in thermal infrared images for autonomous driving[J]. Applied Intelligence, 51, 1244-1261(2021).
[15] Krišto M, Ivasic-Kos M, Pobar M. Thermal object detection in difficult weather conditions using YOLO[J]. IEEE access, 8, 125459-125476(2020).
[16] Song X, Gao S, Chen C. A multispectral feature fusion network for robust pedestrian detection[J]. Alexandria Engineering Journal, 60, 73-85(2021).
[17] Du S, Zhang P, Zhang B et al. Weakand occluded vehicle detection in complex infrared environment based on improved YOLOv4[J]. IEEE Access, 9, 25671-25680(2021).
[18] Wu Z, Wang X, Chen C. Research on light weight infrared pedestrian detection model algorithm for embedded Platform[J]. Security and Communication Networks, 2021, 1549772(2021).
[19] Li S, Li Y, Li Y et al. YOLO-FIRI: Improved YOLOv5 for Infrared ImageObject Detection[J]. IEEE Access, 9, 141861-141875(2021).
[20] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J](2020).
[21] Yang J, Fu X, Hu Y et al. PanNet: A deep network architecture for pan-sharpening[C], 5449-5457(2017).
[22] Han K, Wang Y, Tian Q et al. Ghostnet: More features from cheap operations[C], 1580-1589(2020).
[23] He K, Zhang X, Ren S et al. Spatialpyramid pooling in deep convolutionalnetworks for visual recognition[J]. IEEE transactions on pattern analysis andmachine intelligence, 37, 1904-1916(2015).
[24] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C], 1097-1105(2012).
[25] Simonyan K, Zisserman A. Very deepconvolutional networks for large-scale image recognition[J](2014).
[26] He K, Zhang X, Ren S et al. Deep residual learning for image recognition[C], 770-778(2016).
[27] Howard A G, Zhu M, Chen B et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J](2017).
[28] Zhang X, Zhou X, Lin M et al. Shufflenet: An extremely efficient convolutional neural network for mobile devices[C], 6848-6856(2018).
[29] Sandler M, Howard A, Zhu M et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C], 4510-4520(2018).
[30] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network trainingby reducing internal covariate shift[C], 448-456(2015).
[31] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J](2020).
[32] Huang Z, Wang J, Fu X et al. DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection[J]. Information Sciences, 522, 241-258(2020).
Get Citation
Copy Citation Text
Xian-Guo LI, Ming-Teng CAO, Bin LI, Yi LIU, Chang-Yun MIAO. GPNet:Lightweight infrared image target detection algorithm[J]. Journal of Infrared and Millimeter Waves, 2022, 41(6): 1092
Category: Research Articles
Received: May. 25, 2022
Accepted: --
Published Online: Feb. 6, 2023
The Author Email: Xian-Guo LI (lixianguo@tiangong.edu.cn)