As one of the important tasks in computer vision, target detection has become an important research hotspot in the past 20 years and has been widely used. It aims to quickly and accurately identify and locate a large number of objects of predefined categories in a given image. According to the model training method, the algorithms can be divided into two types: single-stage detection algorithm and two-stage detection algorithm. In this paper, the representative algorithms of each stage are introduced in detail. Then the public and special datasets commonly used in target detection are introduced, and various representative algorithms are analyzed and compared in this field. Finally, the potential challenges for target detection are prospected.
Polar coding gives rise to the first explicit family of codes that provably achieve capacity for a wide range of channels with efficient encoding and decoding. But how fast can polar coding approach capacity as a function of the code length? In finite-length analysis, the scaling between code length and the gap to capacity is usually measured in terms of the scaling exponent µ. It is well known that the optimal scaling exponent, achieved by random binary codes, is µ = 2. It is also well known that the scaling exponent of conventional polar codes on the binary erasure channel (BEC) is µ = 3.627, which falls far short of the optimal value. On the other hand, it was recently shown that polar codes derived from ℓ × ℓ binary polarization kernels approach the optimal scaling exponent µ = 2 on the BEC as ℓ → ∞, with high probability over a random choice of the kernel.Herein, we focus on explicit constructions of ℓ × ℓ binary kernels with small scaling exponent for ℓ 64. In particular, we exhibit a sequence of binary linear codes that approaches capacity on the BEC with quasi-linear complexity and scaling exponent µ < 3. To the best of our knowledge, such a sequence of codes was not previously known to exist. The principal challenges in establishing our results are twofold: how to construct such kernels and how to evaluate their scaling exponent.In a single polarization step, an ℓ × ℓ kernel K ℓ transforms an underlying BEC into ℓ bit-channels W 1 , W 2 , . . . , W ℓ . The erasure probabilities of W 1 , W 2 , . . . , W ℓ , known as the polarization behavior of K ℓ , determine the resulting scaling exponent µ(K ℓ ). We first introduce a class of self-dual binary kernels and prove that their polarization behavior satisfies a strong symmetry property. This reduces the problem of constructing K ℓ to that of producing a certain nested chain of only ℓ/2 self-orthogonal codes. We use nested cyclic codes, whose distance is as high as possible subject to the orthogonality constraint, to construct the kernels K 32 and K 64 . In order to evaluate the polarization behavior of K 32 and K 64 , two alternative trellis representations (which may be of independent interest) are proposed. Using the resulting trellises, we show that µ(K 32 ) = 3.122 and explicitly compute over half of the polarization-behavior coefficients for K 64 , at which point the complexity becomes prohibitive. To complete the computation, we introduce a Monte-Carlo interpolation method, which produces the estimate µ(K 64 ) ≃ 2.87. We augment this estimate with a rigorous proof that µ(K 64 ) < 2.97.
Polar coding gives rise to the first explicit family of codes that provably achieve capacity with efficient encoding and decoding for a wide range of channels. However, its performance at short blocklengths under standard successive cancellation decoding is far from optimal. A well-known way to improve the performance of polar codes at short blocklengths is CRC precoding followed by successive-cancellation list decoding. This approach, along with various refinements thereof, has largely remained the state of the art in polar coding since it was introduced in 2011. Recently, Arıkan presented a new polar coding scheme, which he called polarization-adjusted convolutional (PAC) codes. At short blocklengths, such codes offer a dramatic improvement in performance as compared to CRC-aided list decoding of conventional polar codes. PAC codes are based primarily upon the following main ideas: replacing CRC codes with convolutional precoding (under appropriate rate profiling) and replacing list decoding by sequential decoding. One of our primary goals in this paper is to answer the following question: is sequential decoding essential for the superior performance of PAC codes? We show that similar performance can be achieved using list decoding when the list size L is moderately large (say, L⩾128). List decoding has distinct advantages over sequential decoding in certain scenarios, such as low-SNR regimes or situations where the worst-case complexity/latency is the primary constraint. Another objective is to provide some insights into the remarkable performance of PAC codes. We first observe that both sequential decoding and list decoding of PAC codes closely match ML decoding thereof. We then estimate the number of low weight codewords in PAC codes, and use these estimates to approximate the union bound on their performance. These results indicate that PAC codes are superior to both polar codes and Reed–Muller codes. We also consider random time-varying convolutional precoding for PAC codes, and observe that this scheme achieves the same superior performance with constraint length as low as ν=2.
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