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Pedestrian detection has never been an easy task for computer vision and the automotive industry. Systems like the advanced driver-assistance system (ADAS) highly rely on far-infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what the performance in adverse weather conditions is. In this paper, we introduce a 16-bit thermal data dataset called ZUT (Zachodniopomorski Uniwersytet Technologiczny) as having the widest variety of fine-grained annotated images captured in the four biggest European Union countries captured during severe weather conditions. We also provide a synchronized Controller Area Network (CAN bus) data, including driving speed, brake pedal status, and outside temperature for future ADAS system development. Furthermore, we have tested and provided 16-bit depth modifications for the YOLOv3 deep neural network (DNN) based detector, reaching a mean Average Precision (mAP) up to 89.1%. The ZUT dataset is published and publicly available at IEEE Dataport and Github.

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Round goby Neogobius melanostomus (Pallas 1814) has become a significant component in the diet of piscivorous fish from the Pomeranian Bay (Bornholm Basin, Baltic Sea). Proper identification of fish species in the diet of predators is significant in biological studies of fish and other aquatic animal species, and, with regard to N. melanostomus, it is important to the knowledge of trophic web structures in areas this species has invaded. A total of 142 individuals of N. melanostomus, measuring 16-174 mm standard length, were examined. Seventy-two fishes were caught during monitoring surveys in fishing grounds, whereas 70 were found in the stomachs of European perch Perca fluviatilis, pike-perch Sander lucioperca and Baltic cod Gadus morhua. The objective of the present study was to analyse the sagittal otoliths to identify variations in outer shape with increases in fish length; expand and correct descriptions of the sagitta, lapillus and asteriscus otoliths; and evaluate the relationships among otolith dimensions and fish standard length. The otoliths were described morphologically. The analysis of the outer shape of sagittal otoliths using Fourier analysis and multivariate statistics exhibited great phenotypic variability that was associated with fish length, including within pairs in individuals and/or among individuals in length classes. In addition, the asterisci and lapilli of N. melanostomus from selected specimens, which were described for the first time with regard to fish length, were found to be less variable compared to sagittal otoliths. This study presents the first analysis of intrapopulation phenotypic plasticity of N. melanostomus sagittal otolith morphology as it is linked to fish size.

Pedestrian detection is an essential task for computer vision and the automotive industry. Complex systems like advanced driver-assistance systems are based on far-infrared data sensors, used to detect pedestrians at nighttime, fog, rain, and direct sun situations. The robust pedestrian detector should work in severe weather conditions. However, only a few datasets include some examples of far-infrared images with distortions caused by atmospheric precipitation and dirt covering sensor optics. This paper proposes the deep learning-based data augmentation technique to enrich far-infrared images collected in good weather conditions by distortions, similar to those caused by bad weather. The six most accurate and fast detectors (TinyV3, TinyL3, You Only Look Once (YOLO)v3, YOLOv4, ResNet50, and ResNext50), performing faster than 15 FPS, were trained on 207,001 annotations and tested on 156,345 annotations, not used for training. The proposed data augmentation technique showed up to a 9.38 mean Average Precision (mAP) increase of pedestrian detection with a maximum of 87.02 mAP (YOLOv4). Proposed in this paper detectors’ Head modifications based on a confidence heat-map gave an additional boost of precision for all six detectors. The most accurate current detector, based on YOLOv4, reached up to 87.20 mAP during our experimental tests.