GR-210 - Detection of Small Traffic Signs Using Image Super Resolution

In this study, we propose a novel method to detect small traffic signs that appeared in dashboard camera images. Our method is a framework consisting of the following three distinct algorithms. Grouping Window, Super Resolution Generative Adversarial Network (SRGAN), and a two-stage cascade classifier. Potential regions of interest (ROI) are extracted with Grouping Window which is a sophisticated modification of the traditional sliding window technique. The ROI are upsampled and enhanced using SRGAN. Then the traffic signs among high-resolution ROI are detected and identified by the two-stage cascade classifier of which the first stage filters the ROI that do not contain traffic signs and the second stage classifies the ROI that contains traffic signs into respective classes. The proposed method is capable of detecting traffic signs in the 5-8 square-pixel range. The detection of small objects in this square-pixel range is not generally addressed by state-of-the-art frameworks such as R-CNN and YOLO. We trained our method by using the German Traffic Sign Recognition Benchmark dataset (GTSRB) and tested it on random dashboard camera images containing small traffic signs. The experimental results on 15 random dashboard camera images show that our baseline model localizes 10 of the 23 small traffic signs belonging to the aforementioned pixel range and produces 70 false positives in total. Also, it classifies only one of the detected traffic signs correctly into 43 classes. We plan to improve our method by using image denoising techniques and comparing results

Environmental sanitation development of an enabling policy and legislative environment

Environmental sanitation development of an enabling policy and legislative environmen

An investigation into the analytical, cytotoxicity and immunotoxicity of mycotoxins found in commercially available pelleted pet foods in Durban, South Africa.

Doctoral Degree. University of KwaZulu-Natal, Durban.Introduction: Dry pelleted dog food in the South African market is available via supermarket, pet stores (standard brands - SB) and veterinary channels (premium brands-PB). Similarly, cat food were viewed in two market segments. Methodology: Representative feeds from both categories were analysed for four main mycotoxins viz. aflatoxins (AF), fumonisin (FB), ochratoxin A (OTA), and zearalenone (ZEA) using standard well-described extraction, characterisation and quantitation processes. Results: All foods showed contamination with fungi (mainly Aspergillus flavus, Aspergillus fumigatus and Aspergillus parasiticus) and mycotoxins (the most prevalent being aflatoxins and fumonisins), irrespective of the brand. This study determined the immunotoxicity of extracts from pelleted dog and cat feed for mycotoxins. Isolated dog peripheral blood mononuclear cells (PBMCs) were treated with feed extracts to determine mitochondrial function, oxidative stress, and markers of cell death using luminometry and flow cytometry. Glutathione was significantly depleted by SB extracts. Markers of apoptosis and necrosis were elevated by both SB and PB feeds when compared to controls, with SB extracts being significantly higher than PB. ATP levels decreased with increased mitochondrial depolarization in cells that were exposed to both feed extracts with SB showing the greatest differences when compared to the control. Cat peripheral blood mononuclear cells (PBMCs) were isolated and treated with various feed extracts to determine oxidative stress (TBARS and GSH assay), mitochondrial integrity and cell death (Luminometry and Flow cytometry). Both PB and SB extracts showed significantly decreased ATP levels and increased mitochondrial depolarization except for the PB acid fraction. Lipid peroxidation was significantly increased in both PB and SB extracts with a concomitant decrease in GSH levels. Phosphatidylserine externalization and necrosis levels were increased in both PB and SB extracts when compared to the control. Executioner caspases-3/7 was also elevated following extract exposure except for the PB acid fraction. Conclusion: There were high levels of fungal contamination and mycotoxins in both categories of feed, regardless of the notion that higher priced PB’s were of a higher quality

Salesperson-Sales Manager Social Interaction And Communication Quality: The Impact On Salesperson Cooperation

To get the highest level of performance out of salespeople, companies are searching internally to identify factors that lead to salesperson cooperation. Sales managers create a normative culture that engages the salesperson, which is demonstrated through communication and social interaction. A salesperson who feels connected to the organization is more likely to exert additional effort, such as cooperating with the manager to meet sales objections. The purpose of this paper is to investigate the impact of the salesperson’s social interaction and communication quality with their sales manager on their willingness to cooperate with the manager. The results show that when salespeople interact with their manager in a social setting and discuss non-work related information, salespeople become more willing to cooperate with their manager. Sales manager’s communication quality was not found to have a significant relationship between the salesperson’s willingness to cooperate with the sales manager. Instead, we find that sales manager’s communication quality with the salesperson significantly moderates the relationship between salesperson’s social interaction with the sales manager and salesperson’s willingness to cooperate with the sales manager

Multiclass Classification Using Support Vector Machines

In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes left in the process. In the best case scenario, our algorithm makes a final decision between k classes in O(log2 k) decision steps and in the worst case scenario, DCSVM makes a final decision in k - 1 steps

P2_3 Electric Armour

The validity of using a highly charged capacitor as ‘electric armour’ on light tanks is investigated. The capacitor discharges a large amount of energy into the incoming projectile, causing it to vaporise. It is found that a potential difference of 1kV is required to charge the capacitor to the desirable electric potential energy to vaporise the projectile

P2_6 The Breakfast Club

This paper investigates the sonic boom generated by crunchy foods. Specifically, the force required to tip over an empty cereal box 0.1 m from a person’s mouth is calculated to be 8.37x10^-10 N. It would take the force of 29 million people crunching in unison to generate the required 2.45x10^-2 N to tip over the box

A Multistage Framework for Detection of Very Small Objects

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our two-stage cascade classifier based on two ResNet50 models. The maximum size of the images used for training the proposed framework is 32×32 pixels. The experiments are conducted using rescaled German Traffic Sign Recognition Benchmark dataset (GTSRB) and downsampled German Traffic Sign Detection Benchmark dataset (GTSDB). Unlike MS COCO and DOTA datasets, the resulting GTSDB turns out to be very challenging for any small object detection algorithm due to not only the size of objects of interest but also the complex textures of the background. Our experimental results show that the proposed method detects small traffic signs with an average precision of 0.332 at the intersection over union of 0.3