# Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
## 1 Introduction  
Approximately 125 million households, nearly 7.7 million business establishments, and 90,000 governmental units in the U.S. require efficient and reliable movement of freight [1]. Freight transportation has become an indicator of economic growth and regional development, which makes freight analysis an increasingly important area. The main objective of freight analysis is to reduce freight transit time and transportation cost and improve the reliability of freight movement. In addition, it is beneficial in mitigating traffic congestion, better planning land use, and improving economic competitiveness [2]. Conventional freight data collection occurs through questionnaires filled manually by carriers, shippers, or receivers regarding the commodity type, origin, and destination [2]. However, survey-based methods have several apparent drawbacks, such as low response rates, unknown data reliability, and high time cost [3]. Although trucking companies are likely to keep detailed records of their truck and commodity information, most of them are unwilling to make these records public due to possible competition. As a consequence of the above limitations, current freight data have limited reliability, completeness, and efficiency, thereby reducing its applicability in downstream analysis and processing. The video-based sensing technique has achieved consistent improvements in supporting a cost-effective and accurate traffic system. This has led to increasingly popular vision-based solutions in transportation applications aiming to improve efficiency and reduce costs. However, it is known that applying these solutions to freight classification is still in its infancy due to the challenges of the absence of a public dataset, real-time requirements of identifying objects, and large variations in environmental conditions. Among various transportation modes, truck-based transportation acts as the major mode of commodity shipments in the U.S., carrying 62.7% of the total commodity tonnagesand 61.9% of the commodity values, according to [4]. Truck-based freight transportation is expected to grow in the next decade according to ATA's freight forecast. In response to this, the research community has developed various classification models for trucks and trailers, relying on the input data collected from traffic sensors such as weigh-in-motion (WIM), inductive loop detectors (ILD), and cameras [5; 6; 7; 8]. However, the major limitation is that they fail to reveal the carried cargo from the limited cues identified from trucks. Large-scale road-based freight data analysis is of great need to alleviate problems of traffic congestion, bottlenecks, and truck empty-mile wastage. In this paper, to the best of our knowledge, we present a fundamental video processing approach for freight analysis based on fine-grained visual information of truck images, e.g., logos and texts, collected in real-world environments. Logos provide important cues in identifying commodity types. Preliminary works [9; 10] have shown effectiveness in detecting and recognizing license plates and predefined sets of vehicle brand logos. However, they cannot meet the requirement of freight analysis, where we are interested in reporting a broad range of logos carried by trucks with potential extensions in the future. In other words, the desired approach should be extensible, as it would be impractical to provide an exhaustive list of company logos, and new logos will likely show up. Therefore, an approach to bridging the gap between logo recognition and freight classification is desperately needed to supplement the existing data sources. Prior work has successfully inferred the commodity type based on the trailer types, e.g., enclosed or tank, recognized from some truck images [6]. However, it fails to handle the majority of trucks with enclosed trailers. Fortunately, we might still infer the commodity types by leveraging those company logos potentially on truck bodies, which remains as the non-trivial task of detecting and recognizing logos on trucks.