Application of an Improved Focal Loss in Vehicle Detection

aut.relation.conferenceICAISC: 19th International Conference on Artificial Intelligence and Soft Computingen_NZ
aut.relation.volume12415 LNAIen_NZ
aut.researcherKassabov, Nikola
dc.contributor.authorHe, Xen_NZ
dc.contributor.authorYang, Jen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.contributor.editorRutkowski, Len_NZ
dc.contributor.editorScherer, Ren_NZ
dc.contributor.editorKorytkowski, Men_NZ
dc.contributor.editorPerycz, Wen_NZ
dc.contributor.editorTadeusiewicz, Ren_NZ
dc.contributor.editorZurada, JMen_NZ
dc.description.abstractObject detection is an important and fundamental task in computer vision. Recently, the emergence of deep neural network has made considerable progress in object detection. Deep neural network object detectors can be grouped in two broad categories: the two-stage detector and the one-stage detector. One-stage detectors are faster than two-stage detectors. However, they suffer from a severe foreground-backg-round class imbalance during training that causes a low accuracy performance. RetinaNet is a one-stage detector with a novel loss function named Focal Loss which can reduce the class imbalance effect. Thereby RetinaNet outperforms all the two-stage and one-stage detectors in term of accuracy. The main idea of focal loss is to add a modulating factor to rectify the cross-entropy loss, which down-weights the loss of easy examples during training and thus focuses on the hard examples. However, cross-entropy loss only focuses on the loss of the ground-truth classes and thus it can’t gain the loss feedback from the false classes. Thereby cross-entropy loss does not achieve the best convergence. In this paper, we proposed a new loss function named Dual Cross-Entropy Focal Loss, which improves on the focal loss. Dual cross-entropy focal loss adds a modulating factor to rectify the dual cross-entropy loss towards focusing on the hard samples. Dual cross-entropy loss is an improved variant of cross-entropy loss, which gains the loss feedback from both the ground-truth classes and the false classes. We changed the loss function of RetinaNet from focal loss to our dual cross-entropy focal loss and performed some experiments on a small vehicle dataset. The experimental results show that our new loss function improves the vehicle detection performance.en_NZ
dc.identifier.citationIn: Rutkowski L., Scherer R., Korytkowski M., Pedrycz W., Tadeusiewicz R., Zurada J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science, vol 12415. Springer, Cham.
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dc.subjectFocal Loss; Class Imbalance; Cross-Entropy Loss; RetinaNet; Vehicle Detection; Object Detection; Deep Neural Network
dc.titleApplication of an Improved Focal Loss in Vehicle Detectionen_NZ
dc.typeConference Contribution
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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