Precise Single-stage Detector

There are still two problems in SDD causing some inaccurate results: (1) In the process of feature extraction, with the layer-by-layer acquisition of semantic information, local information is gradually lost, resulting into less representative feature maps; (2) During the Non-Maximum Suppression (NMS) algorithm due to inconsistency in classification and regression tasks, the classification confidence and predicted detection position cannot accurately indicate the position of the prediction boxes. Methods: In order to address these aforementioned issues, we propose a new architecture, a modified version of Single Shot Multibox Detector (SSD), named Precise Single Stage Detector (PSSD). Firstly, we improve the features by adding extra layers to SSD. Secondly, we construct a simple and effective feature enhancement module to expand the receptive field step by step for each layer and enhance its local and semantic information. Finally, we design a more efficient loss function to predict the IOU between the prediction boxes and ground truth boxes, and the threshold IOU guides classification training and attenuates the scores, which are used by the NMS algorithm. Main Results: Benefiting from the above optimization, the proposed model PSSD achieves exciting performance in real-time. Specifically, with the hardware of Titan Xp and the input size of 320 pix, PSSD achieves 33.8 mAP at 45 FPS speed on MS COCO benchmark and 81.28 mAP at 66 FPS speed on Pascal VOC 2007 outperforming state-of-the-art object detection models. Besides, the proposed model performs significantly well with larger input size. Under 512 pix, PSSD can obtain 37.2 mAP with 27 FPS on MS COCO and 82.82 mAP with 40 FPS on Pascal VOC 2007. The experiment results prove that the proposed model has a better trade-off between speed and accuracy.Comment: We will submit it soon to the IEEE transaction. Due to characters limitation, we can not upload the full abstract. Please read the pdf file for more detai

A Lightweight Network for Real-Time Rain Streaks and Rain Accumulation Removal from Single Images Captured by AVs

In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different effect on the captured videos; therefore, they cannot be mitigated in the same way. We propose a lightweight network which mitigates both types of rain degradation in real-time, without negatively affecting the object-detection task. The proposed network consists of two different modules which are used progressively. The first one is a progressive ResNet for rain streaks removal, while the second one is a transmission-guided lightweight network for rain streak accumulation removal. The network has been tested on synthetic and real rainy datasets and has been compared with state-of-the-art (SOTA) networks. Additionally, time performance evaluation has been performed to ensure real-time performance. Finally, the effect of the developed deraining network has been tested on YOLO object-detection network. The proposed network exceeded SOTA by 1.12 dB in PSNR on the average result of multiple synthetic datasets with 2.29× speedup. Finally, it can be observed that the inclusion of different lightweight stages works favorably for real-time applications and could be updated to mitigate different degradation factors such as snow and sun blare

Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks

Актуальность задач обнаружения и распознавания объектов на изображениях и их последовательностях с годами только возрастает. За последние несколько десятилетий предложено огромное количество подходов и методов обнаружения как аномалий, то есть областей изображения, характеристики которых отличаются от прогнозных, так и объектов интереса, о свойствах которых есть априорная информация, вплоть до библиотеки эталонов. В работе предпринята попытка системного анализа тенденций развития подходов и методов обнаружения, причин этого развития, а также метрик, предназначенных для оценки качества и достоверности обнаружения объектов. Рассмотрено обнаружение на основе математических моделей изображений. При этом особое внимание уделено подходам на основе моделей случайных полей и отношения правдоподобия. Проанализировано развитие сверточных нейронный сетей, направленных на задачи распознавания и обнаружения, включая ряд предобученных архитектур, обеспечивающих высокую эффективность при решении данной задачи. В них для обучения используются уже не математические модели, а библиотеки реальных снимков. Среди характеристик оценки качества обнаружения рассмотрены вероятности ошибок первого и второго рода, точность и полнота обнаружения, пересечение по объединению, интерполированная средняя точность. Также представлены типовые тесты, которые применяются для сравнения различных нейросетевых алгоритмовИсследование выполнено при финансовой поддержке РФФИ в рамках научного проекта № 20-17-50020 и частично проекта №19-29-09048