Görsel tespitteki dengesizlik problemlerinin belirlenmesi ve çözümlenmesi

This thesis has two aims: (Aim 1) Identifying imbalance problems in visual detection, and (Aim 2) addressing these problems using loss functions based on performance measures. For Aim 1, we present a comprehensive review of the imbalance problems in object detection including a problem-based taxonomy and a detailed discussion for each problem with its solutions and open issues. To achieve Aim 2, we identify two challenges: (i) Average Precision (AP), the common performance measure, has certain drawbacks. To remedy them, we propose Localisation Recall Precision (LRP) Error as a novel performance measure. (ii) Loss functions derived from performance measures are ranking-based functions whose derivatives are zero or infinite, thus, they cannot directly be used with backpropagation. To overcome this, based on perceptron learning, we propose Identity Update, a simple and general optimisation method for ranking-based losses, which provably ensures balance in terms of total gradient magnitudes of positives and negatives. Having addressed these challenges, using LRP Error and Identity Update, we propose average LRP Loss and Rank & Sort (RS) Loss for balanced training of visual detectors. We show that our loss functions have the following unique benefits: (i) They are easy-to-tune with a single hyper-parameter, different from common methods with ∼ 7 hyper-parameters on average, (ii) they enforce correlation among sub-tasks of visual detectors (i.e. classification and different localisation tasks), which affects both the remaining detections after Non-Maximum- Suppression and performance measure AP, and (iii) they are applicable to a diverse set of visual detectors (i.e. one-stage, multi-stage, anchor-based, anchor-free, with balanced or severely imbalanced data). As a result of these benefits, for example with RS Loss, we train four object detection and three instance segmentation methods only by tuning the learning rate and consistently improve their performance.